Facilitators and barriers to implementing quality measurement in primary mental health care: Systematic review.
Bibliographic record
Abstract
OBJECTIVE To identify facilitators and barriers to implementing quality measurement in primary mental healthcare as part of a large Canadian study (Continuous Enhancement of Quality Measurement) to identify and select key performances measures for quality improvement in primary mental health care.DATA SOURCES CINAHL, EMBASE, MEDLINE, and PsycINFO were searched, using various terms that represented the main concepts, for articles published in English between 1996 and 2005.STUDY SELECTION In consultation with a health sciences research librarian, the initial list of identified references was reduced to 702 abstracts, which were assessed for relevance by 2 coders using predetermined selection criteria. Following a consensus process, 34 articles were selected for inclusion in the analysis. An additional 106 citations were identified in the references of these articles, 14 of which were deemed relevant to this study, for a total of 57 empirical articles identified for review. Most articles described implementation of health care innovations and clinical practice guidelines, 5 focused on quality indicators, and 1 examined mental health indicators.SYNTHESIS Content analysis of the 57 articles identified 7 common categories of facilitators and barriers for implementing innovations, guidelines, and quality indicators: indicator characteristics, promotional strategies,implementation strategies, resources, individual-level factors, organizational-level factors, and external factors.Implementation studies in which these factors were addressed were more likely to achieve successful outcomes.CONCLUSION The overlap in facilitators and barriers across implementation of mental health indicators, healthcare innovations, and practice guidelines is not surprising, as they are often related. The overlap strengthens the findings of the limited number of studies of quality indicators. The Continuous Enhancement of Quality Measurement process for identification and selection of indicators has attended to some of these issues by using a rigorous scientific approach and by engaging a range of stakeholders in selecting and prioritizing the indicators.
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 | 0.108 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".