Holding It Together—Patients' Perspectives on Postoperative Recovery When Using an e-Assessed Follow-Up: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There is an emerging trend to perform surgeries as day surgery. After a day surgery, most of the recovery period takes place at home, and patients are responsible for their own recovery. It has been suggested that electronic health (eHealth) technologies can support patients in this process. A mobile app has recently been developed to assess and follow up on postoperative recovery after a day surgery. OBJECTIVE: The aim of this study was to explore experiences associated with postoperative recovery after a day surgery in patients using a mobile app to assess the quality of their recovery. METHODS: This is a qualitative interview study with an explorative and descriptive design. Participants were recruited from 4 different day surgery units in different parts of Sweden. The study included 18 participants aged >17 years who had undergone day surgery and used the Recovery Assessment by Phone Points, a mobile app for follow-up on postoperative recovery after day surgery. Participants were purposively selected to ensure maximum variation. Semistructured individual interviews were conducted. Data were analyzed using thematic analysis. RESULTS: A total of two themes and six subthemes emerged from the data: (1) the theme Give it all you've got with the subthemes Believing in own capacity, Being prepared, and Taking action, where participants described their possibilities of participating and themselves contributing to improving their postoperative recovery; and (2) the theme The importance of feeling safe and sound with the subthemes Feeling safe and reassured, Not being acknowledged, and Not being left alone, which describe the importance of support from health care professionals and next of kin. CONCLUSIONS: It is important that patients feel safe, reassured, and acknowledged during their postoperative recovery. They can achieve this themselves with sufficient support and information from the health care organization and their next of kin. Using a mobile app, both for assessment and to enable contact with the day surgery unit during the postoperative recovery period, can improve care and create a feeling of not being alone after surgery. We propose that postoperative recovery starts in the prerecovery phase when patients prepare for their recovery to get the best possible outcome from their 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".