Accessible and sustainable prehabilitation: The first stakeholder-informed logic model for prehabilitation programs
Bibliographic record
Abstract
Background: Prehabilitation programs treat modifiable risk factors with the aim of improving surgical outcomes. However, translation of research into practice remains slow. Logic models (i.e., visual representations of how a program works), have the potential to bridge research-to-practice gaps. We aimed to develop a stakeholder–informed logic model for prehabilitation clinics by interviewing stakeholders about 1) what should be the mission, inputs, outputs, and outcomes required to implement and evaluate prehabilitation programs? 2) what specific recommendations should be made to optimize existing prehabilitation programs? Methods: This program evaluation used a qualitative research design and integrated knowledge translation (iKT) concepts to explore stakeholder perspectives. Semi-structured interviews were conducted between June 2022 and December 2023 with stakeholders of an existing prehabilitation clinic of 2 tertiary care hospitals that provide Enhanced Recovery After Surgery (ERAS) care in Montreal, Canada. Interviews were transcribed verbatim and analyzed using manifest summative content analysis (i.e., frequency count) to determine logic model items. Focus groups with stakeholders and prehabilitation staff were conducted throughout the analysis process for member checking. Results: Sixty-one interviews were conducted with prehabilitation staff (n=12), patients (n=10), perioperative physicians (n=10), nurses (n=9), dietitians (n=9), physiotherapists (n=5), and hospital administrators (n=6). Our findings underscored unanimous support for prehabilitation among participants yet revealed challenges hindering efficient resource utilization. Participants were confused regarding the program’s mission and referral process (e.g., who can refer, how to refer, which patients to refer). Priority outcomes varied by stakeholder group: for prehabilitation staff, it was patient adherence to the intervention; for patients, enhanced experience such as feeling cared for and listened to for patients; for inpatient staff and hospital administrators, factors that facilitate discharge. Patient experience and satisfaction (n=32) were described nearly as frequently as clinical outcomes (n=44) such as length of stay.Significance: Through collaborative development of a logic model for prehabilitation with stakeholders, our objective is to enhance the efficiency, accessibility, and sustainability of prehabilitation implementation, while promoting the adoption of stakeholder-driven outcomes for prehabilitation programs globally. Subsequent research should evaluate the application of the logic model to an existing clinic
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".