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Record W4297514736 · doi:10.1080/07448481.2022.2115305

Professor Hippo-on-Campus: Developing and evaluating an educational intervention to build mental health literacy among university faculty and staff

2022· article· en· W4297514736 on OpenAlexaff
Jillian Halladay, Rachel Woock, Annie Xu, Marina Boutros Salama, Catharine Munn

Bibliographic record

VenueJournal of American College Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMental health literacyMental healthMedical educationStigma (botany)PsychologyIntervention (counseling)Health literacyMedicineHealth carePsychiatryMental illness

Abstract

fetched live from OpenAlex

Objective: The development and evaluation of the Professor Hippo-on-Campus Student Mental Health Education Program, a mental health literacy intervention for post-secondary faculty and staff, is described. It includes 3-hour virtual, asynchronous e-modules and an optional 2-hour, synchronous workshop. Participants: All faculty and staff in a single university were invited to participate (February 2020–January 2021). Methods: Pre-and post-module and post-workshop surveys were conducted, assessing knowledge, attitudes, stigma, behavioral intentions, and confidence. Paired t-tests and regressions assessed change. Satisfaction was assessed through closed and open-ended questions, analyzed descriptively and through qualitative content analysis. Results: Four hundred and fifty staff and faculty completed the pre-survey, 262 completed the post-survey, and 122 completed a workshop survey. Participation resulted in improvements in knowledge, attitudes, stigma, and confidence with high levels of satisfaction. Conclusion: The program provides tailored student mental health training to post-secondary staff and faculty, which appears to increase their mental health literacy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.458
Teacher spread0.407 · 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 teacher head, 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

Citations14
Published2022
Admission routes1
Has abstractyes

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