Prospective application of implementation science theories and frameworks to inform use of PROMs in routine clinical care within an integrated pain network
Bibliographic record
Abstract
PURPOSE: The objective of this study is to present the implementation science approaches that were used before implementing electronic patient-reported outcome measures (ePROMs) across an integrated chronic pain network that includes primary, rehabilitation, and hospital-based care. METHODS: The Theoretical Domains Framework (TDF) was used to identify potential barriers and enablers to the use of ePROMS by primary care clinicians. In rehabilitation and tertiary care, the Consolidated Framework for Implementation (CFIR) was used to guide the identification of determinants of implementations, through observation of workflow, patient and clinician surveys, and clinician interviews. A mixed-method concurrent design comprising a quantitative and qualitative analysis was used. The results were reviewed by a steering committee to iteratively inform the ePROM implementation plan. The Proctor framework of evaluation was used to guide the development of an evaluation plan for the implementation of ePROMs in the integrated chronic pain network. RESULTS: Both frameworks provided similar results with respect to healthcare provider knowledge, behaviour, and experience interpreting PROM scores. The TDF and CFIR frameworks differed in identifying organizational-level determinants. The resultant implementation plan was structured around the adoption of PROMs to inform individual treatment planning and quality improvement. The evaluation plan focused on implementation and impact outcomes to evaluate the ePROM intervention. CONCLUSIONS: The TDF and CFIR guided the development of a multi-component knowledge translation and training intervention that will address multiple gaps and barriers to implementation of PROMs across the integrated network. The ePROM intervention will aim to increase clinicians' knowledge and skills and foster best practices.
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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 | 0.304 | 0.348 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".