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Record W3200608373 · doi:10.1080/23774657.2021.1978906

Suffering in Silence: Work and Mental Health Experiences among Provincial Correctional Workers in Canada

2021· article· en· W3200608373 on OpenAlexaffabout
Matthew S. Johnston, Rosemary Ricciardelli, Laura McKendy

Bibliographic record

VenueCorrections · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMental healthPsychologyAnxietySilencePsychiatry

Abstract

fetched live from OpenAlex

In the course of their duties, correctional employees face exposure to a variety of potentially psychologically traumatic events (PPTEs). Recent research points to an array of consequences of work experiences on the psychological well-being of correctional staff, including the development of mental health disorders such as posttraumatic stress disorder, general anxiety disorder, and major depressive disorder. Drawing on an open-ended survey response among provincial and territorial correctional employees (n = 269) in Canada, we consider the experiences of correctional employees who self-report an anxiety, mood, or other mental health disorder, with a particular focus on how such experiences are tied to work conditions and occupational environments. Findings demonstrate that, for many, mental health struggles are intimately tied to both operational and organizational factors – the former referring to job duties and the latter referring to social relations of work. How mental health status is navigated is intimately shaped by occupational norms and meanings tied to mental health, namely stigma. Despite the perceived link between work and mental health outcomes, mental health suffering is understood and responded to as a private problem – with fallout on the personal lives and welfare of staff. We discuss the implications of training paradigms and general understandings of mental health responsibility.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.285
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.335
Teacher spread0.311 · 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.

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

Citations34
Published2021
Admission routes2
Has abstractyes

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