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Record W1257522342

Peer outdoor support therapy (POST) for Australian contemporary veterans: A review of the literature

2014· review· en· W1257522342 on OpenAlexaboutno aff
Kendall Bird

Bibliographic record

VenueJournal of military and veterans' health/Journal of military and veterans' health. · 2014
Typereview
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMental healthPeer reviewPeer supportDistressMental illnessPsychologyMedicinePopulationPsychiatryPsychotherapistPolitical scienceEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Peer outdoor support therapy (POST) is one approach utilised in Canada, the United States and the United Kingdom to address mental illness and distress amongst contemporary veterans. In the current paper several areas of veteran psychological therapeutic treatment are reviewed. Research studies for therapist-led treatments and standard practice recommendations are summarised, then critiqued within the wider literature taking into account unique veteran need and known challenges to treatment which can impact responsiveness, reluctance and retention. Research review results regarding peer support interventions and outdoor therapy interventions for nonveteran and contemporary veteran populations are outlined, alongside an overview of known POST programs for veterans. The implications of the reviewed literature and research are discussed, particularly the need for further research into the role outdoor peer support may play for the Australian veteran population alongside other veteran mental health services.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.118
GPT teacher head0.465
Teacher spread0.348 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2014
Admission routes1
Has abstractyes

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