Self-Worth and Concentration May Be Key Depression Symptoms in University Students

By Taimoor Asghar

Problems with self-worth and concentration may deserve particular attention when depression is assessed in university students. In a 2026 study of 602 university students in Lahore, Pakistan, these symptoms stood out across advanced analyses of the Patient Health Questionnaire-9 (PHQ-9): self-worth and concentration were among the items that best differentiated levels of depressive symptoms, and they were also among the most central symptoms in a symptom network. The findings do not mean that these symptoms cause depression, that other symptoms are unimportant, or that they should be used alone to diagnose anyone. Instead, they show why looking beyond a total depression score can reveal clinically and psychologically meaningful patterns.

The study, published in BMC Psychology, examined medical and non-medical university students using the PHQ-9 and Depression Anxiety Stress Scales-21 (DASS-21). Alongside conventional comparisons and regression models, the researchers used confirmatory factor analysis, item response theory and symptom network analysis. Readers can access the published BMC Psychology study on PHQ-9 and DASS-21 symptom profiles for the complete methods and results.

Why individual depression symptoms matter

Depression questionnaires are often reduced to a single total score. That approach is useful because it provides a practical summary of symptom burden, facilitates screening and allows changes over time to be tracked. But a total score necessarily compresses several different experiences into one number.

Two students with the same PHQ-9 total can have very different symptom profiles. One may primarily report disturbed sleep, fatigue and appetite changes. Another may experience intense negative self-evaluation, difficulty concentrating and persistent low mood. Their numerical totals could be similar even though the difficulties affecting their academic life, relationships and day-to-day functioning are quite different.

This distinction becomes particularly relevant in university settings. Concentration difficulties can directly interfere with lectures, reading, examination preparation and completing assignments. Negative self-worth can affect motivation, social interaction, help-seeking and how students interpret academic setbacks. These effects do not prove that either symptom is the underlying cause of depression, but they illustrate why item-level information may add something that a total score cannot provide.

What the Pakistani university student study examined

The cross-sectional study included 602 undergraduate students from universities in Lahore. Of these, 424 were medical students and 178 were studying non-medical disciplines. Participants completed the PHQ-9 and DASS-21, two widely used self-report measures of psychological symptoms.

The research had two broad aims. First, it examined whether depressive symptoms differed between medical and non-medical students after considering other measured characteristics. Second, it investigated how well the PHQ-9 and DASS-21 performed psychometrically in this population.

The analyses therefore went further than simply calculating average depression scores. They included descriptive statistics, group comparisons, multivariable regression, confirmatory factor analysis, reliability assessment, item response theory, measurement-invariance analysis and symptom network analysis with stability testing.

This combination is useful because each method asks a different question. Reliability examines whether questionnaire items behave consistently as a scale. Factor analysis investigates the structure underlying responses. Item response theory asks how individual items function across different levels of the underlying trait. Network analysis examines relationships among individual symptoms after accounting for the rest of the network.

Self-worth and concentration were among the most discriminating PHQ-9 symptoms

One of the most relevant findings came from item response theory, or IRT. The study used a graded response model to examine the PHQ-9 items. In this analysis, symptoms concerning self-worth, concentration, feeling down and appetite demonstrated the highest discrimination.

In IRT terminology, discrimination describes how strongly an item differentiates between people at nearby levels of the underlying trait being measured. A highly discriminating item changes relatively sharply as the latent level of depressive symptom severity changes. It therefore contributes substantial information about where a respondent may fall on that continuum.

This does not mean that a highly discriminating symptom is necessarily the most severe symptom, the most common symptom or the most clinically dangerous symptom. Those are separate concepts. An item can be highly informative statistically without being the defining feature of depression for every individual.

The interest or anhedonia item showed the lowest discrimination in this particular sample, with a reported discrimination parameter of 0.468. That finding should also be interpreted in context. It does not establish that loss of interest is unimportant in depression. Anhedonia remains a core depressive symptom clinically. The result instead describes how this specific PHQ-9 item behaved statistically in this particular student sample.

Self-worth and concentration were also central in the symptom network

A second analysis approached the symptom data from a different direction. In the network model, self-worth, concentration and downheartedness emerged as the most central nodes.

Network analysis represents symptoms as interconnected nodes. Connections between nodes reflect statistical relationships among symptoms after the specified model accounts for other variables in the network. Centrality measures are then used to describe how strongly a symptom is positioned within that system of associations.

The fact that self-worth and concentration appeared prominently in both IRT and network analyses is particularly interesting. IRT and network models do not measure exactly the same property. IRT evaluates how individual questionnaire items perform in relation to an underlying symptom continuum, whereas network analysis focuses on relationships among symptoms. When the same symptoms stand out under different analytical frameworks, that convergence can make them worthy of closer investigation.

However, centrality must not be confused with causality. A central symptom in a cross-sectional network cannot automatically be described as a symptom that causes the others. The network was based on measurements collected at one period rather than repeated observations showing how symptoms changed over time. Longitudinal or experimental evidence would be needed before making stronger causal claims.

Why self-worth may be especially meaningful in student depression

University life involves repeated evaluation. Students receive grades, compare performance with peers, compete for opportunities and make decisions that may influence their careers. In that environment, negative beliefs about personal worth can become intertwined with perceptions of academic success or failure.

The PHQ-9 self-worth item captures negative self-evaluation rather than ordinary disappointment after a poor result. Clinically relevant negative self-worth can involve persistent feelings of inadequacy, failure or excessive self-criticism. For a student, these feelings may influence how an ordinary setback is interpreted: a disappointing examination may be viewed not simply as a poor performance but as evidence of personal inadequacy.

The Pakistani study cannot establish that university pressures produced the observed self-worth symptoms. It did not track participants prospectively or experimentally manipulate academic stress. Nevertheless, identifying self-worth as both a highly discriminating PHQ-9 item and a central network node suggests that negative self-evaluation may represent an especially informative component of depressive symptom patterns in this population.

For university mental-health services, the practical implication is not to replace established screening with a single question about self-worth. Instead, a positive depression screen may warrant a conversation about the student’s actual symptom pattern. Understanding whether negative self-evaluation is prominent can provide more context than simply knowing the total score.

Why concentration problems can be easy to overlook

Concentration difficulties are particularly relevant for students because cognitive work occupies a large proportion of university life. A student who cannot sustain attention while reading, following a lecture or preparing for an examination may initially interpret the problem as laziness, lack of discipline or poor study technique.

Concentration difficulty is not specific to depression. Insufficient sleep, anxiety, stress, substance use, attention disorders, physical illness, medication effects and many other factors can affect attention. For that reason, concentration problems alone cannot identify depression.

Within a broader depressive symptom pattern, however, difficulty concentrating can be important. The Lahore study found that the concentration item was among the most discriminating PHQ-9 symptoms and among the most central nodes in the network. That makes concentration particularly relevant when considering how depression may present in an academic environment.

This also illustrates why mental-health symptoms and academic functioning can overlap. A student seeking help for declining academic performance may not initially describe the problem as a mental-health concern. Asking about mood, sleep, motivation, self-evaluation and concentration can help reveal whether difficulties extend beyond study habits.

Low mood and appetite also provided substantial information

The findings should not be reduced to self-worth and concentration alone. Item response theory also identified the PHQ-9 items related to feeling down and appetite change among those with the highest discrimination.

This reinforces the idea that depression is multidimensional at the symptom level. Emotional symptoms, cognitive symptoms and bodily or behavioral symptoms can all provide useful information. The value of studying individual items is precisely that it avoids assuming every symptom behaves identically just because all contribute to the same questionnaire total.

The study’s network analysis similarly identified downheartedness alongside self-worth and concentration as a central symptom. Taken together, the findings suggest that cognitive and emotional experiences occupied prominent positions in the observed symptom structure.

What the PHQ-9 can and cannot tell us

The PHQ-9 is a brief questionnaire originally developed to assess nine depressive symptom domains. Its validation literature has supported its usefulness as a depression severity measure, and its brevity has contributed to widespread use in clinical care and research.

But a screening questionnaire is not equivalent to a complete diagnostic assessment. A PHQ-9 score can identify elevated depressive symptom burden and help clinicians or researchers quantify severity, but diagnosis requires appropriate clinical evaluation and consideration of context, duration, functional impairment, differential diagnoses and other relevant factors.

Item-level analyses can improve understanding of how the instrument behaves, yet they do not turn individual items into diagnostic tests. A student who reports poor concentration may not have depression. Likewise, someone experiencing depression may have relatively little difficulty concentrating while experiencing other prominent symptoms.

One PHQ-9 item addresses thoughts related to death or self-harm. Regardless of whether that item is statistically central or highly discriminating in a particular dataset, such thoughts can carry immediate clinical importance. Statistical rankings should never be used to dismiss symptoms whose safety implications require assessment.

The broader study findings provide useful context

The symptom-level findings were part of a larger investigation. Non-medical students in the sample reported somewhat higher depressive symptoms than medical students. Their median PHQ-9 score was 10 compared with 9 among medical students, and adjusted analyses also found higher PHQ-9, DASS depression and DASS anxiety scores among non-medical students. The DASS stress difference was not statistically significant in the adjusted model.

At the same time, the statistical models explained only a small proportion of the variation in symptom scores, with reported R-squared values ranging from 0.029 to 0.041. This is an important qualification. Academic discipline alone cannot explain student mental health, and the findings should not be interpreted as showing that being a medical or non-medical student determines who will experience depression.

Female gender was associated with higher scores across the measured domains in the study, while each additional reported hour of sleep was associated with a lower PHQ-9 score. Previous treatment for depression was the strongest measured predictor of PHQ-9 scores in the reported model. These are associations from observational data, not proof that changing any single measured factor will necessarily produce a particular change in depressive symptoms.

What did the psychometric analyses show?

The researchers reported Cronbach’s alpha coefficients ranging from 0.82 to 0.88 across the evaluated scales, indicating good internal consistency in this sample. The DASS-21 confirmatory factor analysis also demonstrated adequate overall model fit, with a comparative fit index of 0.954, Tucker-Lewis index of 0.948 and root mean square error of approximation of 0.052.

One caution emerged from the DASS-21 analysis: latent correlations among depression, anxiety and stress were extremely high. The reported depression-stress correlation was 0.939 and the anxiety-stress correlation was 0.949. Such substantial overlap raises questions about how distinctly the three DASS domains separated in this population, even though overall model fit was acceptable.

Measurement invariance across academic discipline was supported, which strengthens the interpretation of medical versus non-medical comparisons because it indicates that the measures operated sufficiently similarly across those groups under the tested models.

How much confidence should we place in the network findings?

Network analysis is useful for studying relationships among symptoms, but its results depend on sample size, model specification and statistical stability. The authors therefore conducted stability testing rather than presenting centrality rankings without qualification.

The reported stability coefficients ranged from 0.31 to 0.44. These findings provide some support for the identified network structure while also encouraging restraint in making strong claims about the exact ranking of individual symptoms. In practical terms, self-worth and concentration emerged as noteworthy signals, but the results should be replicated in independent samples before they are treated as universally central features of student depression.

This is especially important because symptom networks may differ across populations. University students in Pakistan may experience different social, educational, economic or cultural circumstances from students elsewhere. Even within Pakistan, findings from universities in Lahore cannot automatically be generalized to every institution, region or student group.

What this could mean for university mental-health screening

The study supports a broader principle: screening can be more informative when clinicians and support services look at the pattern behind the total score.

• Examine individual symptoms as well as the total. Two students with identical PHQ-9 scores may require very different conversations about what is affecting them.
• Ask about academic functioning. Concentration difficulty may become visible through missed deadlines, inability to study efficiently or reduced classroom engagement.
• Explore persistent negative self-evaluation. Strong feelings of inadequacy or failure may be clinically meaningful rather than simply evidence of ordinary examination stress.
• Do not ignore other symptoms. Low mood, appetite changes, sleep disturbance, fatigue, loss of interest, psychomotor changes and thoughts of death or self-harm remain relevant parts of a complete assessment.
• Interpret screening in context. Scores should be considered alongside functional impairment, duration, medical history, psychological history and other possible explanations for symptoms.

For researchers, the findings also argue for preserving item-level data rather than reporting only prevalence estimates or mean scale scores. Modern psychometric approaches can reveal whether particular questionnaire items behave differently across severity levels or occupy distinctive positions within a symptom system.

What the study does not prove

Several limitations are essential when interpreting the headline finding.

Central symptoms are not necessarily causal symptoms

Self-worth and concentration were central in a cross-sectional statistical network. This cannot establish that improving either symptom would automatically improve the entire depression network.

High discrimination does not equal clinical importance

An IRT discrimination parameter describes measurement performance. Clinical urgency is a different concept. A symptom with lower statistical discrimination may still be critically important for an individual patient.

The results describe one population

The sample consisted of university students in Lahore, Pakistan. Replication in other Pakistani regions, countries, age groups and clinical populations is needed before assuming that the same symptom hierarchy will appear everywhere.

Cross-sectional associations cannot establish direction

For example, depression might contribute to sleep disturbance, inadequate sleep might worsen mood, or both could be influenced by other factors. A cross-sectional study cannot determine the direction of these relationships.

From depression scores to depression profiles

The most useful message from this research is not that researchers have discovered two symptoms that matter while the others do not. The more meaningful conclusion is that depression questionnaires contain information at more than one level.

The total PHQ-9 score summarizes overall symptom burden. Individual responses describe the patient’s or participant’s symptom profile. IRT can show which items provide more measurement information at different points on the underlying continuum. Network analysis can show how symptoms are statistically connected. These perspectives complement rather than replace one another.

For university students, the prominence of self-worth and concentration is intuitively relevant because both can intersect strongly with academic and social functioning. The study provides empirical evidence that these symptoms also stood out statistically in a Pakistani student sample. Future longitudinal research could investigate whether changes in these symptoms precede, follow or move alongside changes in other depressive symptoms.

Until then, the findings are best treated as a reason to pay closer attention to symptom profiles rather than as justification for targeting two symptoms in isolation.

Conclusion

Self-worth and concentration emerged as particularly informative depression symptoms among 602 Pakistani university students. Both were among the PHQ-9 items with high discrimination in item response theory and among the most central symptoms in network analysis, alongside downheartedness. These converging findings suggest that cognitive difficulties and negative self-evaluation may be especially useful areas to examine when trying to understand how depressive symptoms are experienced by university students.

The findings should nevertheless be interpreted within their limits. They come from a cross-sectional sample, network centrality does not demonstrate causation, and statistical importance does not substitute for clinical judgment. Depression remains a heterogeneous condition in which the complete pattern of symptoms, functional impact, safety concerns and individual circumstances matter.

Medical disclaimer: This article is for educational and research communication purposes only and is not a diagnosis or a substitute for assessment by a qualified healthcare professional. Persistent depressive symptoms, substantial impairment in daily functioning, or thoughts of self-harm or suicide warrant prompt professional assessment. Anyone in immediate danger should seek urgent local emergency assistance.