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Record W3170115339 · doi:10.22215/cpopp.v7i.3179

Using Skills, Not Pills

2021· article· en· W3170115339 on OpenAlexaboutno aff
Matt Bologna

Bibliographic record

VenueCarleton Perspectives on Public Policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMental healthMental health literacyFunctional illiteracyCompetence (human resources)MedicineNursingPsychologyMental illnessPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Mental health illiteracy in primary care is a growing concern for policymakers. Given the generalist nature of the primary health care practice, these physicians may lack the specialized knowledge and the time to identify and treat mental illnesses appropriately. Mental health literacy interventions are either contact-based, intensive social-contact-based, or skills-based. Although contact-based and intensive social-contact interventions have shown positive improvements in reducing stigma in the short-term, their efficacy for clinical behavioural change is questionable. This paper argues that skills-based interventions are more appropriate mental health literacy interventions for health care settings, using three Canadian case studies to highlight their potential to foster positive improvements to physician confidence and competence when treating mental illness. These interventions require more research with larger sample sizes to determine their efficacies for wide-scale implementation across jurisdictional boundaries and health specializations.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.105
GPT teacher head0.468
Teacher spread0.363 · 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.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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