Patient narratives of illnesses requiring abdominal surgery in Cambodia: Heroic/stoic, and dealing with ‘the ball of meat’
Bibliographic record
Abstract
This study describes the illness narratives that inform treatment-seeking behaviours for acute abdominal conditions in Cambodia, and thereby explores factors impeding the timely delivery of surgical intervention. Semi-structured qualitative interviews were undertaken with patients who had undergone abdominal surgery at Siem Reap Provincial Hospital between 2011 and 2014. Interviews collected basic demographic information and also patient narratives based on Groleau’s McGill Illness Narrative Interview (MINI). Interviews were contemporaneously translated from Khmer to English and recorded for transcription. A content analysis of interview transcripts based on narrative enquiry was undertaken. Ninety-seven patients participated in the study and five themes emerged from the data. These were: Explanatory models about the causes of abdominal pain and effects of surgery; Pre-surgery stoicism and illness management; Fear of poor outcomes and death; Burden of treatment costs and anticipated recovery time; and, Enhancing community trust in surgery. Our findings add the patient voice to the limited evidence about access to surgery, and socio-cultural and financial barriers affecting treatment-seeking behaviours in Cambodia. By understanding the collective narratives surrounding experiences of abdominal surgery, efforts to improve surgical services in Cambodia may be better informed of the reasons patients delay treatment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".