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Record W2728474343 · doi:10.35502/jcswb.47

Re-integration: a new standard in first responder peer support

2017· article· en· W2728474343 on OpenAlexvenueaboutno aff
Glen Klose, Colleen Mooney, Doug McLeod

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthEmulationPublic relationsCommissionService (business)Subject (documents)Political scienceGuidelinePeer supportMedical educationPsychologyMedicineNursingBusinessLibrary scienceLawComputer scienceSocial psychologyMarketingPsychiatry

Abstract

fetched live from OpenAlex

Since its inception, the Edmonton Police Service (EPS) Re-integration Program has grown in its capacity, impact, and service to members within EPS. It has also attracted increasing attention among—and emulation by—other first responder communities in the province of Alberta. Most recently, the program was the subject of a featured segment during the joint Canadian Association of Chiefs of Police (CACP) and Mental Health Commission of Canada (MHCC) international conference, “The Mental Health of Police Personnel: What We Know & What We Need to Know and Do”, held in February 2017. Based on the strong reception and interest generated among conference delegates, the Journal of CSWB invited the program’s architects to develop the following Practice Guideline article, with a view to bringing wider awareness to this unique peer-supported program. The EPS program connects conventional counselling and support resources with aspects of recovery and re-integration that are more closely tied to the equipment and operational realities of first responders.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.145
GPT teacher head0.432
Teacher spread0.287 · 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 designObservational
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

Citations6
Published2017
Admission routes2
Has abstractyes

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