What are the most important dimensions of quality for addiction and mental health services from the perspective of its users?
Bibliographic record
Abstract
There is a need to better engage service users in improving their experience with the care received in Addiction and Mental Health (A&MH). Dimensions of patient experience that are most salient to A&MH service users still remain to be properly defined from the patient perspective. This research focuses on identifying key domains of service experience important to patients of Addiction and Mental Health using patient focus groups. In addition, through a patient and family advisory committee, patients were also engaged as co-partners of the research team. The patient advisors had a major role in overseeing the research project, assisting with the thematic analysis and identifying the service domains. A total of 48 individuals (60% female; mean age = 45 years) with lived experience using A&MH services participated in the focus groups. The major themes that emerged from the focus groups led to the identification of seven dimensions of service quality: 1) access, 2) humanity of care, 3) skill and quality of staff, 4) patient engagement, 5) internal and external program communication, 6) individualized treatment and 7) continuity of care. We found that these domains were similar across all service settings including addictions. Patient advisors provided a unique “insider” perspective on the data. Identifying common aspects of service is the first phase of this study. These findings will form the framework for the development of a patient experience survey for Addiction and Mental Health.
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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".