Nurses’ Perception of a Tailored Web-Based Intervention for the Self-Management of Pain After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgeries rank among the most frequent surgical procedures and present a risk of chronic postsurgical pain (CPSP). A continuum approach is required to prevent the development of CPSP. As a first step, a tailored web-based intervention was developed and successfully tested to tackle pain management during hospitalization. Before proceeding to further development, preliminary acceptability of the intervention including the postdischarge phase must be evaluated. PURPOSE: The purpose of this study was to examine nurses' perception of a tailored Web-based intervention for pain management in the early recovery phase. The objectives were to evaluate intervention's acceptability and to identify ways to enhance its acceptability. METHODS: A parallel mixed methods approach was used to assess the acceptability of the intervention in the early recovery phase (first month after surgery). RESULTS: In total, 249 participants completed the online survey and 10 participants were individually interviewed. Overall, the intervention was rated as acceptable. No difference was found in acceptability ratings by demographics. The intervention was rated as appropriate to very much appropriate by 79% of participants. Although nurses seemingly would recommend it to their patients, they did not perceive that their patients would be as highly willing to use it. Interviews highlighted several strengths of the intervention, such as postoperative pain awareness, customization of content, and flexible dosage and schedule. However, the main weakness was related to patient adherence. Opting for a hybrid format and integrating individual preferences could enhance the coaching experience. CONCLUSION: The innovative intervention was judged as acceptable for pain management in the early recovery phase. Considering that the intervention has demonstrated positive effects on the pain experience in the first week after cardiac surgery, it seems logical to explore its potential impact after discharge on the transition to CPSP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".