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Record W4416620118 · doi:10.1080/13668803.2025.2591788

The non-uniformed TAC member: exploring the impact of tactical team membership on family systems and family well-being

2025· article· en· W4416620118 on OpenAlexaffabout
Zachary Towns, Rosemary Ricciardelli

Bibliographic record

VenueCommunity Work & Family · 2025
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFamily systemsSystems analysisFamily systems theory

Abstract

fetched live from OpenAlex

The rate at which police tactical (TAC) officers have contact with direct exposure to potentially psychologically traumatic events (PPTEs) and physical/psycho-emotional risk is higher compared to other police officers due to the nature of their work. To interpret these impacts, we bring the perspectives of n = 24 full-time TAC officers from two large-scale urban police services in Canada to the forefront by using semi-structured interviews to unpack the synergistic relationship between TAC membership and family stress. The findings from the current article suggest that occupational risk can affect the well-being of TAC officers and how structural lifestyle demands tied to logistics, identities and risk can have synergistic impacts on TAC officers and their families. We conclude with suggestions for police leadership, emphasizing how TAC families may be at risk of experiencing psychological stress tied to their loved ones’ occupation.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.072
GPT teacher head0.393
Teacher spread0.321 · 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 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
Published2025
Admission routes2
Has abstractyes

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