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Record W3177201183 · doi:10.21061/jvs.v7i1.227

The Politics of Treatment: A Qualitative Study of Canadian Military PTSD Clinicians

2021· article· en· W3177201183 on OpenAlexaffabout
John Whelan, Maya Eichler, Deborah Norris, Denise Landry

Bibliographic record

VenueJournal of Veterans Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsBureaucracyPsychological interventionNegotiationQualitative researchPoliticsPsychologyMedicinePsychiatryPsychotherapistClinical psychologyPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

There has been an upsurge in post-traumatic stress disorder (PTSD) research, but these efforts have not included trauma clinicians. Using a constructivist grounded research methodology, we examined clinicians’ views about military PTSD, their experiences in utilizing accepted interventions, and the personal impacts of this work. Our findings indicate that clinicians struggle with conceptualizations of PTSD, accepted treatments, and the requirements of navigating the Veterans Affairs Canada (VAC) bureaucracy. Demands to negotiate occupational realities while attempting care for clients underpinned experiences of emotional exhaustion. Contrasting the literature on secondary trauma, bureaucratic forces, implied expert status, and lack of supports for clinicians were at the root of exhaustion. Military trauma clinicians appear caught in the politics of treatment with detrimental effects on their health. This study is the first to explore clinician views on the benefits and costs of working with military trauma survivors.

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)0.018
metaresearch head score (Gemma)0.041
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 score0.471
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0350.017
Scholarly communication0.0090.005
Open science0.0040.008
Research integrity0.0040.007
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 head0.355
GPT teacher head0.543
Teacher spread0.188 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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