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Record W3008194265 · doi:10.22215/etd/2019-13638

Medical Narratives of Military PTSD: Moving Beyond the Biomedical Approach

2019· dissertation· en· W3008194265 on OpenAlexaffabout
Kishelle Reid

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsNarrativeStigma (botany)PositivismNarrative inquiryMental healthPsychologyHealth careConstructivist grounded theoryQualitative researchPolitical sciencePsychotherapistMedicinePsychiatrySociologySocial scienceGrounded theoryLaw

Abstract

fetched live from OpenAlex

Barriers to care among soldiers within the Canadian Armed Forces is a topic in need of recognition, with the majority of Canadian soldiers failing to seek healthcare for a mental health issue.Most current literature comes from the medical and psychological disciplines, using quantitative research methodologies to identify specific factors such as stigma, which act as barriers to care.In order to move beyond this current positivist paradigm, there must be a collaboration between realist and constructivist frameworks.This study utilized a qualitative constructivist approach to analyse the narratives of Canadian psychiatrists surrounding the PTSD diagnosis within the military.Analysis of these narratives sought to address the power relations, construction, and framework of this diagnosis.Finally, these medical PTSD narratives were compared to military PTSD narratives in order to identify how well they served those suffering from this medical disorder.

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)0.009
metaresearch head score (Gemma)0.017
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 score0.612
Threshold uncertainty score0.781

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0210.031
Scholarly communication0.0090.006
Open science0.0020.008
Research integrity0.0020.004
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 head0.042
GPT teacher head0.391
Teacher spread0.350 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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