Co-Developed Logic Model for Surgical Prehabilitation in an Acute Care Setting: A Qualitative Study of Stakeholders’ Perspectives
Bibliographic record
Abstract
Introduction: Prehabilitation programs treat modifiable risk factors to improve surgical outcomes. However, translation of research into practice remains challenging. Logic models, visual representations of how a program works, have the potential to bridge research-to-practice gaps. We aimed to develop a logic model for prehabilitation programs in tertiary care centers by interviewing stakeholders about what should be the mission, inputs, outputs (activities and participants), and targeted outcomes for prehabilitation. METHODS: A multi-site qualitative study was conducted from June 2022 to December 2023. Interviews were analyzed using manifest summative content analysis to determine logic model items. Focus groups for member checking were performed with stakeholders throughout the analysis process. RESULTS: Sixty-one interviews were conducted with stakeholders: prehabilitation staff (n = 12), patients (n = 10), perioperative care physicians (n = 10), nurses (n = 9), dietitians (n = 9), physiotherapists (n = 5), and hospital administrators (n = 6). Findings underscored unanimous support for prehabilitation yet revealed challenges that hindered efficient and equitable resource utilization, which have been addressed in the logic model. To evaluate the success of prehabilitation, both clinician- (n = 44) and patient-oriented outcomes (n = 32) were valued by stakeholders; however, priority outcomes varied by stakeholder group: intervention adherence (prehabilitation staff), experience and satisfaction (patients), and facilitation of discharge (clinicians and hospital administrators). CONCLUSION: This co-developed logic model was designed to improve the efficiency, accessibility, and sustainability of acute care prehabilitation programs by offering a detailed blueprint. Researchers and clinicians can draw on the insights from this co-production process to develop, implement, and evaluate their own prehabilitation 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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".