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Record W4283698470 · doi:10.2196/34168

Peer-Presented Versus Mental Health Service Provider–Presented Mental Health Outreach Programs for University Students: Randomized Controlled Trial

2022· article· en· W4283698470 on OpenAlexaffvenue
Laurianne Bastien, Bilun Naz Böke, Jessica Mettler, Stephanie Zito, Lina Di Genova, Vera Romano, Stephen P. Lewis, Rob Whitley, Srividya N. Iyer, Nancy L. Heath

Bibliographic record

VenueJMIR Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of GuelphMcGill University
Fundersnot available
KeywordsMental healthPeer supportOutreachService providerMedical educationPsychologyRandomized controlled trialMedicineNursingService (business)Psychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: University students are reporting concerning levels of mental health distress and challenges. University mental health service provider initiatives have been shown to be effective in supporting students' mental health, but these services are often resource-intensive. Consequently, new approaches to service delivery, such as web-based and peer support initiatives, have emerged as cost-effective and efficient approaches to support university students. However, these approaches have not been sufficiently evaluated for effectiveness or acceptability in university student populations. OBJECTIVE: Thus, the overarching goal of this study was to evaluate a mental health service provider-presented versus peer-presented web-based mental health resilience-building video outreach program against a wait-list comparison group. METHODS: Participants were 217 undergraduate students (mean age 20.44, SD 1.98 years; 171/217, 78.8% women) who were randomly assigned to one of the intervention groups (mental health service provider-presented: 69/217, 31.8%; peer-presented: 73/217, 33.6%) or the wait-list comparison group (75/217, 34.6%). Participants in the intervention groups were asked to watch 3 brief skill-building videos addressing strategies for building mental health resilience, whereas the comparison group was wait-listed. The mental health service provider-presented and peer-presented video series were identical in content, with presenters using a script to ensure consistency across delivery methods, but the videos differed in that they were either presented by mental health service providers or university students (peers). All participants were asked to complete web-based self-report measures of stress, coping self-efficacy, social support, social connectedness, mindfulness, and quality of life at baseline (time 1), 6 weeks later (time 2, after the intervention), and 1-month follow-up (time 3). RESULTS: Results from a series of 2-way ANOVAs found no significant differences in outcomes among any of the 3 groups. Surprisingly, a main effect of time revealed that all students improved on several well-being outcomes. In addition, results for program satisfaction revealed that both the mental health service provider-presented and peer-presented programs were rated very highly and at comparable levels. CONCLUSIONS: Thus, findings suggest that a web-based mental health resilience-building video outreach program may be acceptable for university students regardless of it being mental health service provider-presented or peer-presented. Furthermore, the overall increases in well-being across groups, which coincided with the onset and early weeks of the COVID-19 pandemic, suggest an unexpected pattern of response among university students to the early period of the pandemic. Limitations and barriers as well as research implications are discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT05454592; https://clinicaltrials.gov/ct2/show/NCT05454592.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.048
GPT teacher head0.428
Teacher spread0.379 · 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 designRandomized trial
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".

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Citations12
Published2022
Admission routes2
Has abstractyes

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