Patients’ experiences of enhanced recovery after surgery: a systematic review of qualitative studies
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To aggregate, interpret and synthesise findings from qualitative studies to further our knowledge regarding patients' pre- and postoperative experiences when participating in an enhanced recovery after surgery. BACKGROUND: Numerous quantitative studies have documented benefits of participation in enhanced recovery after surgery programmes. Randomised control trials show that enhanced recovery after surgery reduce patient morbidity and shorten hospital length of stay. However, we presently have only sparse knowledge regarding patients' experiences of participating in these programmes. DESIGN: A qualitative systematic review and meta-synthesis. METHODS: A systematic literature search of databases (Cinahl, Medline, PsycINFO, Ovid Nursing, and EMBASE) for qualitative studies published between 2000-2014 were undertaken. The identified studies were critically evaluated using the Critical Appraisal Skills Program, and patient experiences were synthesised into new themes by a team of researchers, using qualitative content analysis. RESULTS: Eleven studies were included. Upon analysis, four main themes emerged: information transfer, individualised treatment vs. standardised care, balancing burdensome symptoms and expectations for rapid recovery, and sense of security at discharge. Information helped patients feel secure and prepared for surgery. Patients reported being motivated to participate in their recovery process. However, this became challenging when they faced symptoms such as pain, nausea, and weakness. Professional support fostered a feeling of security that was important in helping patients continue their regimen, recover, and be discharged as early as planned. CONCLUSIONS: Patients in enhanced recovery after surgery programmes desired more consistency between pre- and postoperative information. Important opportunities exist to improve symptom management and help patients feel more secure about recovery postoperatively. RELEVANCE TO CLINICAL PRACTICE: Nurses are in a unique position to improve communication of standardised regimens and enhance symptom management across the perioperative period. Clinical outreach, such as follow-up visits or phone calls, could target older adults who need additional assistance to meet enhanced recovery after surgery programme goals and derive benefit.
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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 | 0.099 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".