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Record W4200102029 · doi:10.1080/07448481.2021.2007112

Increasing resiliency and reducing mental illness stigma in post-secondary students: A meta-analytic evaluation of the inquiring mind program

2021· review· en· W4200102029 on OpenAlexafffundabout
Andrew C. H. Szeto, Laura Henderson, Brittany L. Lindsay, Stephanie Knaak, Keith S. Dobson

Bibliographic record

VenueJournal of American College Health · 2021
Typereview
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMental Health Commission of CanadaUniversity of Calgary
FundersUniversity of CalgaryCommission de la santé mentale du Canada
KeywordsMental healthStigma (botany)Mental illnessPromotion (chess)PsychologyHelp-seekingRandomized controlled trialPsychiatryClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Objective: Worsening student mental health, along with more complex mental illness presentation and increased access to campus mental health services, has led to a mental health “crisis” on campuses. One way to address student mental health needs may be through mental health programs which have been found to increase resiliency and help-seeking, and reduce stigma. Participants: The effectiveness of The Inquiring Mind (TIM), a mental health promotion and mental illness stigma reduction program, was examined in 810 students from 16 Canadian post-secondary institutions. Methods and Results: Using a meta-analytic approach, TIM improved resiliency and decreased stigmatizing attitudes from pre to post, with medium effect sizes (d > .50). Analyses with those that completed the follow-up (about one-third of the sample) showed that effects were mostly retained at three months. Other outcomes also point to the program’s effectiveness. Conclusion: TIM appears to be an effective program for post-secondary students. However, additional research, including randomized control trials, is needed to address study limitations.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.011
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.153
GPT teacher head0.521
Teacher spread0.368 · 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 designMeta-analysis
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

Citations15
Published2021
Admission routes3
Has abstractyes

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Same venueJournal of American College HealthSame topicMental Health Treatment and AccessFrench-language works237,207