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Record W7028657758

Everyone goes home: Exploring the implicit learning of critical incidents in the volunteer fire service

2019· article· en· W7028657758 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2019
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthConstruct (python library)Service (business)PsychoeducationSocial constructionismMental health service
DOInot available

Abstract

fetched live from OpenAlex

<p>Volunteer firefighters make up approximately 85% of the fire service in Canada (Haynes, 2016) and almost the entire fire service on Prince Edward Island. As a result of this voluntary service, many firefighters are exposed to traumatic events and critical incidents, which may lead to poor mental health outcomes. Training and education in the area of critical incidents was shown to be lacking in a sample of 100 volunteer firefighters on PEI, with 67.6% indicating they have never been educated on mental health issues in the fire service (Brazil, 2017). Through a social constructionist theoretical lens, this study describes how volunteer firefighters informally learn and socially construct critical incidents and how to manage them. The findings suggest that there is a traditional culture into which firefighters are socialized and that the cultural values and norms that are implicitly learned lend to the informal learning pertaining to critical incidents. What is most notable about this study is that the traditional hyper-masculine fire service culture was found to be evolving, including the social constructs that contribute to its institutionalization. As such, firefighters are undergoing an informal re-education pertaining to critical incidents and mental health, which bodes well for the introduction of psychoeducation within the fire service.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.300
Teacher spread0.264 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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