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Record W4243364001 · doi:10.32920/ryerson.14663442

Exploring the experience of receiving Canadian Armed Forces’ mental health services

2021· preprint· en· W4243364001 on OpenAlexaffabout
Erin O’Rourke

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsMental healthThematic analysisStigma (botany)PsychologyNarrativeNarrative inquirySocial stigmaPopulationRelevance (law)Qualitative researchPublic relationsMedicineSociologyPsychiatryPolitical scienceFamily medicineEnvironmental healthSocial science

Abstract

fetched live from OpenAlex

This study uses a narrative approach to explore the experience of receiving mental health services within the Canadian Armed Forces (CAF). Data was collected from media sources where interviews were conducted with current and former CAF members about their experiences with the CAF’s mental health services. Thematic narrative analysis was used to interpret themes that emerged within participants’ stories and to identify similarities and differences across stories. Findings included the experience of structural difficulties when accessing the CAF’s mental health services, the negative effects of mental health stigma, fears related to disclosing issues of mental health and the need for changes to the CAF’s mental health system. The study also presents a preliminary discussion on the relevance of anti-oppressive social work practice for the CAF’s mental health services. Also detailed is the process of completing the research including the challenges encountered when attempting to access the population and recruit participants.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0300.012
Scholarly communication0.0080.003
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.219
GPT teacher head0.421
Teacher spread0.202 · 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 designQualitative
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 routes2
Has abstractyes

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