Selection of Pediatric Mental Health Quality Measures for Health System Improvement in British Columbia Based on a Modified Delphi Approach
Bibliographic record
Abstract
Background The COVID-19 pandemic has highlighted the importance of mental wellbeing. The identification and implementation of quality measures can improve health outcomes and patient experience. The objective was to identify and define a core set of valid and relevant pediatric mental health quality measures that will support health system evaluation and quality improvement in British Columbia, Canada. Methods The study consisted of four phases. First, a comprehensive database search identified valid pediatric quality measures focused on mental health and substance use (MH/SU). Second, the identified quality measures were mapped to focus areas, which were then prioritized by two stakeholder groups consisting of 26 members. Third, up to two representative measures for each prioritized focus area were pre-selected by an expert panel ( n = 9). And fourth, a three-step modified Delphi approach was employed to (1) assess each quality measure on a 7-point Likert scale against three relevance criteria (representative of a quality problem, value to intended audience and actionable), (2) discuss the results, and (3) select and rank the most relevant measures. Forty-eight stakeholders were invited to participate; of those 24 completed the round 1 survey, 21 participated in the round 2 discussion and 18 voted in the round 3 selection and ranking survey. For round 1, consensus was determined when at least 70% of the response rates were within the range of five to seven. For round 3, Kendall's coefficient of concordance W was used as an estimator of inter-rater reliability. Results One-hundred pediatric mental health quality measures were identified in the database search. Of those, 37 were mapped to ten focus areas. Pre-selection resulted in 19 representative measures moving forward to the Delphi study. Eleven measures met the consensus thresholds and were brought forward to the round 2 discussion. Round 3 ranking showed moderate to strong raters' agreement (Kendall's W = 0.595; p < 0.01) and resulted in the following five highest-ranked measures: level of satisfaction after discharge from inpatient admission due to MH/SU, number of patients experiencing seclusion or restraint, length of time from eating disorder referral to assessment, number of ED visits due to MH/SU, and number of readmissions to ED. Conclusion The selected core set of valid and relevant pediatric quality measures will support sustainable system change in British Columbia. The five top-ranked measures will be refined and tested for data collection feasibility before being implemented in the province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".