A qualitative study of patient education needs for hip and knee replacement
Bibliographic record
Abstract
BACKGROUND: Quality health information is key to patient engagement, self-management and an enhanced healthcare experience. There is strong evidence to support involving patients and their families in the development and evaluation of health-related educational material. These factors were the impetus for our high volume joint replacement centre to undertake a qualitative study to elicit patient experiences to inform the development of effective strategies and education along the care continuum for hip and knee replacement. METHODS: Purposively selected patients from postoperative follow-up clinics were recruited to participate in a focus group or telephone interview. We developed a semi-structured interview guide that addressed four specific aspects of the patient's experience with educational material: pre-surgery, hospital stay, recovery period and future recommendations. The focus groups and interviews continued to the point of saturation and were audio-recorded and transcribed verbatim. Interview transcripts were coded and then inductively organized into larger categories using thematic analysis. RESULTS: Six focus groups and seven telephone interviews were conducted, totalling 32 participants. One of the key themes that emerged was a need for more education concerning pain management post-operatively; specifically, patients wanted more information on expected levels of pain, pain medication usage, management of side effects and guidelines for weaning off the medication. There was surprising variability in patients' descriptions of their pre-surgery, surgery and recovery experiences. These corresponded to an equally diverse range of preferences for educational content, delivery and timing. Many patients reported using the web while others preferred traditional formats for information delivery. There was some interest in receiving education using mobile technology. CONCLUSIONS: Our findings validate the importance of multi-modal patient education tailored to individual preferences and experiences, which may differ according to such characteristics as gender and age. The gap in pain management information is a critical finding for healthcare providers working with patients undergoing joint replacement. Developing pain management education in different formats that addresses frequently asked questions will enhance patient engagement and, their overall experience and recovery.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".