Enhanced recovery after surgery in Pakistan: a qualitative descriptive analysis of current practices and future directions
Bibliographic record
Abstract
Enhanced Recovery After Surgery (ERAS) is a cost-effective perioperative approach that has been shown to shorten patients' hospital length of stay, improve resource utilization, and reduce postoperative costs for both patients and hospitals. While ERAS has the potential to offer even greater benefits in low- and middle-income countries (LMICs) its successful long-term implementation remains incomplete in Pakistan. This study aimed to explore insights and identify opportunities for implementing ERAS within the local socio-environmental context. A qualitative descriptive approach was employed, using convenience sampling to recruit 11 surgical residents from a public tertiary care hospital in Lahore, Pakistan. Individual semi-structured interviews were conducted. The data collected was then thematically analyzed to capture the residents' experiences regarding the implementation of ERAS. Acknowledging the benefits of ERAS, participants faced several challenges when implementing ERAS in their respective wards. The participants identified several key opportunities for successful implementation, including enhanced teamwork and collaboration amongst medical teams, improved patient education and compliance towards ERAS, strengthening of peripheral healthcare services, and targeted resource allocation. Even though several challenges identified by the participants were like those highlighted in high-income countries (HICs), unique barriers specific to Pakistan's healthcare structure and culture also emerged. Further research exploring and highlighting these specific challenges is needed to overcome these core barriers and promote a shift towards a standardized healthcare system focused on improving patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".