Challenges in providing ethically competent health care to incarcerated older adults with mental illness: a qualitative study exploring mental health professionals’ perspectives in Canada
Bibliographic record
Abstract
BACKGROUND: The population of incarcerated older adults is the fastest growing demographic in prisons. Older persons in custody have poorer health as compared with those in the community. The unmet and complex health care needs of incarcerated older adults with mental illness raise justice, safety, dignity and fairness in care as ethical concerns. As there exists research gap to better understand these concerns, the current study aimed at exploring the perspectives of mental health professionals on challenges in delivering ethically competent care to mentally ill incarcerated older adults in Canada. METHODS: Thirty-four semi-structured interviews were conducted between August 2017 and November 2018 with prison mental health professionals in Canada who were selected using purposive and convenience sampling techniques. The audio recorded interviews were transcribed verbatim and analysed inductively to generate themes. RESULTS: The results were distilled into three main categories and seven subcategories that related to ethical issues in the provision of health care for mentally ill incarcerated older adults. The main categories included imprisoned older persons with special care needs, lack of resources, and the peer-support program. CONCLUSIONS: Results of this study showed that existing practices of care of mentally ill incarcerated older adults are characterised by challenges that increase their vulnerability to worse health conditions. It is imperative for local authorities, policy makers and representatives to prepare for and respond to the challenges that compromise ethically competent health care for, and healthy ageing of, mentally ill incarcerated older adults.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".