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Record W7132896994

Identification and Assessment of Adverse Occupational Health Risks Among Public Safety Personnel: A Biopsychosocial Investigation of Stress Biomarkers in Naturalistic Settings

2023· dissertation· W7132896994 on OpenAlexaff
Jennifer Chan

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiopsychosocial modelStressorOccupational stressAllostatic loadMental healthOccupational safety and healthSubclinical infection
DOInot available

Abstract

fetched live from OpenAlex

Chronic occupational stress is one of the most significant contributors to adverse health and performance outcomes among public safety personnel (PSP). This dissertation builds upon prior research on the allostatic load (AL) model of stress and health, investigating PSP’s occupational stress, health, and performance in naturalistic settings from a biopsychosocial perspective across three components: a) AL from occupational stressors and adverse health outcomes (study 1–2), b) the impact of subclinical health outcomes on occupational performance (study 3), and c) the exploration of an emerging cardiac metric to address current biomarker literature gaps and confounds (study 4). Study 1 examined occupational stress subcomponents and their association with mental health; findings revealed that organizational in contrast to operational stress was the strongest occupational subcomponent associated with adverse mental health risk. Building upon prior research, study 2 added an objective stress biomarker (i.e., diurnal cortisol) to investigate occupational factors related to physical health; findings indicated that as operational risk increased, dysregulated neuroendocrine function was more pronounced. Study 3 combined biopsychosocial variables of stress and AL (e.g., occupational stress, mental health symptoms, cortisol) to investigate potential associations with a timely and urgent behavioural outcome among PSP — lethal force decision-making; findings revealed that dysregulated neuroendocrine function (when including outlier data) increased the odds of lethal force decision-making errors. Together, study 2 and 3’s findings demonstrated the strengths and weaknesses of AL neuroendocrine biomarkers among PSP in naturalistic settings. Building on that knowledge, study 4 investigated an emerging cardiac biomarker (heart rate fragmentation — HRF) that may better capture AL. Study 4 found that HRF can detect acute psychological stress in both healthy individuals and those with probable mental health (pMH) symptoms; while pMH and healthy individuals did not differ in baseline HRF, exploratory analyses revealed that pMH individuals had significantly blunted HRF stress reactivity. Overall, this dissertation demonstrates significantly prevalent health and performance risks associated with acute and occupational stress in otherwise healthy, nonclinical adults. A further novel contribution, is the introduction of an emerging cardiac biomarker with promise for detecting AL risk among PSP; knowledge may inform future occupational interventions and policy for adverse health prevention.

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)0.004
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.070
GPT teacher head0.416
Teacher spread0.346 · 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
Published2023
Admission routes1
Has abstractyes

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