Exploratory study of patients’ and carers’ preferences for postdischarge surgical wound monitoring using survey and interviews
Bibliographic record
Abstract
OBJECTIVES: To explore patients' and carers' preferences for postdischarge surgical wound monitoring. DESIGN: Explanatory mixed methods study with an online survey followed by online interviews. SETTING: The online survey was distributed via the Cardiothoracic Interdisciplinary Research Network and cardiac surgery patient and public involvement groups in London and Leicester, UK. Participants were invited to share the survey link with other patients and carers. Interviewees were recruited through the survey. PARTICIPANTS: Seventy participants completed the survey: 74% patients and 26% carers. A range of ages, sex, ethnicities and geographical locations were represented. Six survey patient participants volunteered to be interviewed. FINDINGS: Themes identified were the impact on patients of having a surgical site infection, patients' preferences for postdischarge surgical wound follow-up, access to specialist support, wound monitoring using digital technology and receiving information from the hospital about wounds and wound care. Interviewees described feeling isolated after discharge from hospital and 10% of survey patient respondents, including four of the six interviewees, reported hospital readmissions. Survey respondents' preferred routes for providing hospitals with wound information were over the telephone (30%), emails (24%), text messages (16%) and photos sent securely (14%). All six interviewees' preference was for digital approaches using images. Survey respondents were least likely (50%) to reply to questionnaires that required software to be downloaded and installed. Interviewees considered digital wound monitoring to be convenient and the best use of patient and staff resources. A new theme was identified where patients wanted to become more involved in treating their surgical wounds at home. CONCLUSION: Experiences described by participants suggests there is a need to improve post-discharge wound monitoring. A new approach should be proactive, ongoing and provide easy access to healthcare services. Digital surgical wound monitoring offers these benefits and is acceptable to patients. TRIAL REGISTRATION NUMBER: ISRCTN13950775; Post-results.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".