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Record W4398161828 · doi:10.1177/07340168241256348

The Moral Impacts of Organizational Stress on Correctional Officers

2024· article· en· W4398161828 on OpenAlexaffabout
Rosemary Ricciardelli, Matthew S. Johnston, Brittany Mario

Bibliographic record

VenueCriminal Justice Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPsychologyStress (linguistics)Social psychologyCriminologyApplied psychology

Abstract

fetched live from OpenAlex

Organizational stress (i.e., structural aspects of the organization such as excessive workload, shiftwork, gossip) has long been found by public safety personnel to be more impactful on their health and wellness than operational stress (i.e., inherent stresses of the job such as altercations, intervention in suicide behaviors). In the current study, which engages semi-structured interviews conducted with 28 correctional officers employed at one provincial prison in Atlantic Canada, we unpack through a lens of moral distress four prevalent sources of organizational stress among correctional officers that emerged in the data without categories precogitated, with a focus on participant experiences and expressed similarities across accounts: (1) management, (2) staff retention, (3) training needs, (4) lack of mental health support. Findings indicate organizational stress has a significant impact on correctional officers and these sources of organizational stress are exacerbated by officers’ moral and ethical vulnerabilities emergent from their conditions of employment. We recommend several practical changes to ease the strains and moral harms felt by correctional officers and better support their mental health and well-being, such as increasing staffing levels, providing more education and training opportunities for frontline officers and senior leaders, and providing more adequate mental health support for correctional officers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.041
GPT teacher head0.359
Teacher spread0.319 · 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
GenreReview

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

Citations11
Published2024
Admission routes2
Has abstractyes

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