Benefits of Digital Mental Health Care Interventions for Correctional Workers and Other Public Safety Personnel: A Narrative Review
Bibliographic record
Abstract
Chronic exposure to stressors and potentially psychologically traumatic events contributes to the high prevalence of mental health disorders in correctional workers (CWs) and other public safety personnel (PSP). Digital mental health interventions are an accessible and scalable method of improving and maintaining the mental health of this population. The current review explores the benefits of digital mental health interventions for PSP-with a focus on CWs-and how these innovations can address the limitations in in-person mental health care. A systematic literature search of five databases (Medline, PsycInfo, Embase, CINAHL, Google Scholar) was conducted until March 2022. The search yielded 16 publications that focused on digital mental health interventions or care available to CWs and other PSP. The benefits of digital innovations were summarized into five categories which discussed (1) their ability to enhance accessibility and reduce stigma; (2) the provision of evidence-based and structured psychotherapy programs; (3) variability in the degree of therapist engagement; (4) the integration of proactive interventions; and (5) enhancing engagement by acknowledging unique experiences and interpersonal relationships. Although digital mental health technologies for CWs are still in their infancy, there is strong evidence to support their effectiveness in ameliorating symptoms of mental distress. Future research should consider how ethnicity, gender, culture, sexual orientation, and socioeconomic status can be integrated into these therapies and how the interplay between different stakeholders and organizations can impact the effectiveness of online therapies and programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".