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Record W4389435475 · doi:10.3390/bs13121002

Depression and Anxiety among Undergraduate Health Science Students: A Scoping Review of the Literature

2023· review· en· W4389435475 on OpenAlexaff
Gerald Agyapong-Opoku, Belinda Agyapong, Gloria Obuobi-Donkor, Ejemai Eboreime

Bibliographic record

VenueBehavioral Sciences · 2023
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsAnxietyCINAHLDepression (economics)Mental healthSocioeconomic statusMEDLINEScopusClinical psychologyPopulationPsychologyMedicineSystematic reviewPsychiatryEnvironmental healthPsychological intervention

Abstract

fetched live from OpenAlex

Background: Health science students in post-secondary institutions experience high levels of depression and anxiety due to increased stress levels, workload, low socioeconomic status, and history of family mental illness, among other factors. Given the significant negative impact that depression and anxiety can have on undergraduate health science students, it is essential to understand the prevalence and correlation of these conditions in this population. In light of this, this scoping review aims to identify, document, and analyze the literature on the prevalence and determinants of anxiety and depression among undergraduate health sciences students and identify gaps in knowledge for future research. Methods: This scoping review was planned and executed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for the Scoping Reviews statement. A comprehensive and systematic search was carried out for five databases, namely MEDLINE, Scopus, EMBASE, CINAHL, and PubMed. Results: From the literature identified by our search strategy, the lowest prevalence for anxiety was 5.8%, and the highest was 82.6%, with a median of 44.25%. The prevalence of depression ranged from a high of 88.8% to a low of 2.1%, with a median value of 34.8%. Our analysis revealed that correlates of anxiety and depression among health science students include sociodemographic factors such as age, sex, gender, relationships, ethnicity, and family history, personal health conditions, and academic and socioeconomic issues. Conclusions: With the high incidence of anxiety and depression among health science students, there is an increasing need to find practical remedies to support these students. It is also essential for policymakers and university authorities to implement interventions such as supportive text messages and other strategies geared toward providing support and improving the psychological well-being of health science students.

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)0.012
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0210.016
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.267
GPT teacher head0.599
Teacher spread0.331 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations45
Published2023
Admission routes1
Has abstractyes

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