Quality measures for primary mental healthcare: a multistakeholder, multijurisdictional Canadian consensus
Bibliographic record
Abstract
OBJECTIVE: To develop quality measures using a consensus-based, multistakeholder process to improve delivery of mental health services within primary healthcare settings. METHODS: A three-stage consensus model culminating in a two-round, modified Delphi postal survey ranking quality measures according to 'actionability,' relevance and overall importance. PARTICIPANTS: More than 800 people from all 10 provinces and three territories in Canada participated in the study, representing consumers/advocates, clinicians, academics and government decision-makers from regional, provincial and federal levels. A small group with expertise in First Nations and rural-setting health issues was also included, as well as international experts. RESULTS: The top overall pan-Canadian measure was 'Education about Depression.' 'Actionability' was a key criterion for many of the top 30 measures. Fifty per cent of these measures focused on three major themes: depression, self-harm and access to a broader spectrum of treatment (such as outreach services and psychotherapy). Additional themes included the need for greater collaboration, respectful treatment of patients and families, and improved evaluation of patients. One-way ANOVA results indicated statistically significant differences (p <0.05) between academics, clinicians, consumers and decision-makers on approximately 5% of quality measure ratings. The majority (85% of the 5%) of these differences involved consumer stakeholders. CONCLUSION: A small set of specific consensus measures were identified through a rigorous, evidence-informed process. These measures can be used for system-wide changes or at the individual practice level. Although these measures have been developed within a Canadian context, the methodology utilised and the measures selected can be adapted elsewhere.
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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 | 0.302 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".