Benefits and barriers to engagement of mental health caregivers in advisory roles: Results from a cross‐sectional survey
Bibliographic record
Abstract
Background and Aims: Mental health institutions and community organizations have had difficulty recruiting patients and caregivers onto their Patient, Family, and Community Advisory Committees (PFACs). Previous research has focused on barriers and enablers of engaging patients and caregivers who have advisory experience. This study acknowledges the experiential difference between patients and caregivers by focusing only on caregivers; further, we compare the barriers and enablers between advising versus non-advising caregivers of loved ones with mental illness. Methods: = 44 non-advising caregivers). Results: Caregivers were disproportionately female and late middle-aged. Advising and non-advising caregivers differed on employment status. There were no differences of the demographics of their care-recipients. More non-advising caregivers reported being hindered from PFAC engagement by family-related duties and interpersonal demands. Finally, more advising caregivers considered being publicly acknowledged as important. Conclusions: Advising and non-advising caregivers of loved ones with mental illness were similar in demographics and in reporting the enablers and hindrances that impact PFAC engagement. Nevertheless, our data highlights specific considerations that institutions/organizations should consider when recruiting and retaining caregivers on PFACs. Patient or Public Contribution: This project was led by a caregiver advisor to address a need she saw in the community. The surveys were codesigned by a team of two caregivers, one patient, and one researcher. The surveys were reviewed by a group of five caregivers external to the project. The results of the surveys were discussed with two caregivers involved directly with the project.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".