Older adults’ postoperative pain medication usage after total knee arthroplasty: A qualitative descriptive study
Bibliographic record
Abstract
OBJECTIVE: Most total knee arthroplasty (TKA) recipients experience pain following the procedure. Patients are provided with medications to manage pain but there is little information regarding their usage of analgesics after hospital discharge. This study investigated analgesic usage in recent TKA recipients. DESIGN AND PARTICIPANTS: A qualitative descriptive approach was taken to produce a summary of the experiences of 14 participants. Purposive sampling methods were used during recruitment. One semistructured interview was conducted with each participant. Interviewing continued until theoretical saturation was reached. RESULTS: Most participants used less medication than was prescribed and stopped taking prescription analgesics before requiring a renewal. Participants adjusted their usage in response to pain, adverse effects, advice from their family and healthcare providers, fears of becoming "hooked," and a general dislike of taking medications. CONCLUSIONS: Patient modifications to medication regimens are often labeled as patient nonadherence; however, participants in this study considered their actions to be adaptive. This conceptual distinction has practical implications for healthcare providers. These findings emphasize the importance of having TKA patients develop their pain management regimen in conjunction with healthcare providers so that regimens can be tailored to individual needs.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".