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Record W4402889214 · doi:10.4103/dypj.dypj_33_24

Mental Health Trajectories in First-Year Undergraduate University Students

2024· article· en· W4402889214 on OpenAlexaboutno aff
Fayaz Ahmad Paul

Bibliographic record

VenueD Y Patil Journal of Health Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyMathematics educationMedical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

There is a need to address mental health issues among college and university students. The transition to university aligns with the prime period for the emergence of mental illnesses.[1] Approximately 75% of mental illnesses manifest throughout young adulthood, with anxiety and depression being the most prevalent, collectively referred to as internalizing disorders.[2] Internalizing disorders are psychological problems characterized by the inward direction of emotions and thoughts, typically including feelings of sadness, loneliness, stress, and worry.[3] During adolescence and young adulthood, it is more prevalent to experience mental health issues and distressing symptoms that do not meet the criteria for a complete diagnosis.[3] Emergent adulthood, which spans from age 16 to 25 years, is a phase characterized by rapid brain development.[4] This leads to heightened vulnerability to external stimuli.[4] Furthermore, this period is characterized by significant psychosocial development and a greater sense of independence.[5] University students face various risk factors for anxiety and depression, such as financial stress, separation from family and close friends, and adapting to new learning methods and academic expectations.[6] Nevertheless, the presence of flexible schedules and convenient on-campus resources enables students to easily get expert assistance. Additionally, students have the chance to establish fresh social connections.[7] Thus, while the transition to university can be a critical time for the development of internalizing symptoms and disorders, it also presents possibilities for resilience and prevention.[7] The prevalence of internalizing disorders among students attending Western colleges is estimated to be around 20%, with a range of 10%–84% depending on the assessment methods employed.[7,8] There is a consistent upward trend in the prevalence of internalizing symptoms among university students in Canada, the UK, and India, these figures appear to be on the rise.[9,10] Both university students and individuals of the same age in the general population are more likely to experience internalizing illnesses if they are female and have a lower socioeconomic status.[10] Alcohol and substance usage are correlated with elevated levels of internalizing symptoms.[11] Research studies have shown that experiencing early life adversities, such as parental divorce and abuse, is associated with an increased likelihood of developing anxiety and depression during emerging adulthood, regardless of cultural background. However, there is insufficient study, especially focused on students in this area.[12,13] Conversely, social support and university connectedness, which refer to an individual’s subjective sense of belonging and integration within the university campus and student community, have been recognized as elements that provide protection.[14] Research studies carried out on groups of people within a community have found connections between being female, having a higher socioeconomic status, having more social support, and experiencing gains in mental health during the transition to adulthood.[15] Conversely, issues with peers, substance abuse, and a family background of depression seem to be indicators of a gradual rise in depressive symptoms.[16] Among students, it has been seen that neuroticism, academic stress, and social connectedness can predict patterns of adjustment.[17] Additional research has emphasized the enhancements in the psychological well-being of students who receive assistance from trained professionals or treatments.[18] Acknowledgments None. Author contribution Fayaz Ahmad Paul: Conceptualization and design, writing, proofreading, and preparation of the final draft. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.424
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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