Service user and family member perspectives on services for mental health, substance use/addiction, and violence: a qualitative study of their goals, experiences and recommendations
Bibliographic record
Abstract
BACKGROUND: Mental health and substance use disorders (MSD) are significant public health concerns that often co-occur with violence. To improve services that address MSD and violence [MSD(V)], it is critical to understand the perspectives of those most affected, people who have sought help for MSD(V) (i.e., "service users"), especially those with co-occurring issues, as well as their family members. METHODS: We conducted structured interviews with 73 service users and 41 family members of service users in two Ontario communities (one urban, one rural) regarding their goals related to help-seeking, positive and negative experiences, and recommendations for improving systems of care. RESULTS: Overall, participants expressed a need for services that: (1) are respectful, nonjudgmental, and supportive, help service users to feel more 'normal' and include education to reduce stigma; (2) are accessible, varied and publicly funded, thereby meeting individual needs and addressing equity concerns at a systems level; and (3) are coordinated, holistic and inclusive of family members who often support service users. CONCLUSIONS: The findings provide a rich understanding of how service users and their families perceive services for MSD(V) issues and identify key ways to better meet their needs.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".