Development and Evaluation of a Novel Patient-Reported Outcome Implementation Process in a Student-Led Pro Bono Clinic: Acceptability and Adoption
Bibliographic record
Abstract
Background and Purpose. Although patient-reported outcomes (PROs) are increasingly implemented to inform care and demonstrate the value of care across disciplines and settings, no previous reports have assessed PRO implementation and acceptability to student clinicians in a pro bono physical therapy clinic. The purpose of this case report was to describe the development and evaluation of an implementation process for an electronically administered PRO in a pro bono physical therapy clinic. Case Description. A student-faculty team used the consolidated framework for implementation research to identify barriers to PRO implementation in one student-run pro bono clinic and develop strategies to address identified barriers. The change management theoretical framework was used to develop an implementation process that addressed all general and local contextual needs. Acceptability and adoption of the Patient-Reported Outcome Measurement Information System Physical Function (PROMIS PF) computer adaptive test to student clinicians were assessed to evaluate implementation effectiveness. Outcomes. Implementation barriers included rotating student teams, limited knowledge, need for workflow modifications, and PRO translation to Spanish. Student clinician training, workflow adaptation, and use of English and Spanish versions of PROMIS PF addressed all barriers, general and local contextual needs. Student clinician responses suggested that implementation strategies were acceptable, citing increase efficiency and simplicity. Patient-Reported Outcome Measurement Information System Physical Function adoption was 81% (per month range = 47–100%) over the 6-month implementation period. Discussion and Conclusion. Acceptance and adoption rates by student clinicians indicate that implementation of PROMIS PF was successful at improving PRO collection and influencing student clinician education about PROs.
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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 | 0.087 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".