Barriers and Facilitators for Implementation of a Patient Prioritization Tool in Rehabilitation Settings
Bibliographic record
Abstract
Abstract Introduction. Prioritization tools aim to manage access to care by ranking patients equitably in waiting list based on determined criteria. Patient prioritization has been studied in a wide variety of clinical health services, including rehabilitation contexts. We created a web-based patient prioritization tool with the participation of stakeholders in two rehabilitation programs, which we aim to implement into clinical practice. Successful implementation of such innovation can be influenced by a variety of determinants. The goal of this study was to explore facilitators and barriers to the implementation of a patient prioritization tool in rehabilitation programs.Methods. We used two questionnaires and conducted two focus groups among service providers from two rehabilitation programs. We used descriptive statistics to report results of the questionnaires and qualitative content analysis based on Consolidated Framework for Implementation Research.Results. Key facilitators are the flexibility and relative advantage of the tool to improve clinical practices and produce beneficial outcomes on patients. Main barriers are the lack of training, financial support and human resources to sustain the implementation process.Conclusion. This is the first study that highlights organizational, individual and innovation levels facilitators and barriers for the implementation of a prioritization tool from service providers’ perspective.Contributions to the literatureMethods used in this study could be operationalized in future studies to investigate barriers and facilitators of the implementation of an innovative intervention in rehabilitation settings.We used a well-known implementation framework (CFIR) to classified the determinants of the implementation, which could help to compare the results with other similar studies in implementation science.The barriers and facilitators identified in this study are an important first step in the implementation process of a patient prioritization tool in rehabilitation programs.
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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 | 0.059 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".