A protocol for the formative evaluation of the implementation of patient-reported outcome measures in child and adolescent mental health services as part of a learning health system
Bibliographic record
Abstract
Abstract Background: Mental health conditions affect one in seven young people and research suggests that current mental health services are not meeting the needs of most children and youth. Learning health systems are an approach to enhancing through rapid, routinized cycles of continuous learning and improvement. Patient-reported outcome measures provide a key data source for learning health systems. They have also been shown to improve outcomes for patients when integrated into routine clinical care. However, implementing these measures into health systems is a challenging process. This paper describes a protocol for a formative evaluation of the implementation of patient-reported measures in a newly operational child and adolescent mental health centre in Calgary, Canada. The purpose is to optimize the collection and use of patient-reported outcome measures. Our specific objectives are to assess the implementation progress, identify barriers and facilitators to implementation, and explore patient, caregivers and clinician experiences of using these measures in routine clinical care. Methods: This study is a mixed-methods, formative evaluation using the Consolidated Framework for Implementation Research. Participants include patients and caregivers who have used the centre’s services, as well as leadership, clinical and support staff at the centre. Focus groups and semi-structured interviews will be conducted to assess barriers and facilitators to the implementation and sustainability of the use of patient-reported outcome measures, as well as individuals’ experiences with using these measures within clinical care. The data generated by the patient-reported measures over the first five months of the centre’s operation will be analyzed to understand implementation progress, as well as validity of the chosen measures for the centres’ population. Discussion: The findings of this evaluation will help to identify and address the factors that are affecting the successful implementation of patient-reported measures at the centre. They will inform the co-design of strategies to improve implementation with key stakeholders, which include patients, clinical staff, and leadership at the centre. To our knowledge, this is the first study of the implementation of patient-reported outcome measures in child and adolescent mental health services and our findings can be used to enhance future implementation efforts in similar settings.
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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 | 0.251 | 0.219 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.060 | 0.016 |
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".