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Record W3034770683 · doi:10.3389/fpsyg.2020.01036

Understanding Needs, Breaking Down Barriers: Examining Mental Health Challenges and Well-Being of Correctional Staff in Ontario, Canada

2020· article· en· W3034770683 on OpenAlexafffundabout
Rosemary Ricciardelli, R. Nicholas Carleton, James Gacek, Dianne Groll

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of ReginaQueen's UniversityMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsMental healthPsychologyWell-beingApplied psychologyMedical educationPsychiatryPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Mental health challenges appear to be extremely problematic among correctional service employees, affecting persons working in community, institutional, and administrative correctional services. Focusing specifically on giving voice to correctional workers employed by the Ontario Ministry of Community Services and Corrections, we shed light on their interpretations of the complexities of their occupational work and of how their work affects staff. We show that participants encounter barriers to treatment seeking, which they describe as tremendous, starting with benefits, wages, and shift work. We let the voices of staff elucidate what is needed to create a healthier correctional workforce. Recommendations include more training opportunities and programs; quarterly, semi-annual, or annual appointments with a mental health professional who can assess changes in the mental health status of employees; off-site assessments to ensure confidentiality; and team building opportunities to reduce inter-personal conflict at work and increase moral by improving the work environment.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.067
GPT teacher head0.306
Teacher spread0.239 · 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 designQualitative
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
Published2020
Admission routes3
Has abstractyes

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