Patients’ perspectives of prehabilitation as an extension of Enhanced Recovery After Surgery protocols
Bibliographic record
Abstract
BACKGROUND: Enhanced Recovery After Surgery (ERAS) and prehabilitation programs are evidence-based and patient-focused, yet meaningful patient input could further enhance these interventions to produce superior patient outcomes and patient experiences. We conducted a qualitative study with patients who had undergone colorectal surgery under ERAS care to determine how they prepared for surgery, their views on prehabilitation and how prehabilitation could be delivered to best meet patient needs. METHODS: We conducted semistructured interviews with adult patients who had undergone colorectal surgery under ERAS care within 3 months after surgery. Patients were enrolled between April 2018 and June 2019 through purposive sampling from 1 hospital in Alberta. The interview transcripts were analyzed independently by a researcher and a trained patient-researcher using inductive thematic analysis. RESULTS: Twenty patients were interviewed. Three main themes were identified. First, waiting for surgery: patients described fear, anxiety, isolation and deterioration of their mental and physical states as they waited passively for surgery. Second, preparing would have been better than just waiting: patients perceived that a prehabilitation program could prepare them for their operation if it addressed their emotional and physical needs, provided personalized support, offered home strategies, involved family and included surgical expectations (both what to expect and what is expected of them). Third, partnering with patients: preoperative preparation should occur on a continuum that meets patients where they are at and in a partnership that respects patients' expertise and desired level of engagement. CONCLUSION: We identified several patient priorities for the preoperative period. Integrating these priorities within ERAS and prehabilitative programs could improve patient satisfaction, experiences and outcomes. Actively engaging patients in their care might alleviate some of the anxiety and fear associated with waiting passively for surgery.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".