Nurses Perspectives on Low-Dose Methadone for Pain in Nursing Homes: Semi-structured Interviews
Bibliographic record
Abstract
Background:Chronic pain is prevalent in nursing homes, yet safe and effective long-acting opioid options are limited. Studies suggest that low-dose methadone (LDM) may be an ideal alternative. However, its use in nursing homes remains rare and perspectives from nursing staff on its practical benefits and challenges are underreported. Objectives:To explore nurses’ perspectives on LDM for pain in nursing home residents and assess potential benefits and barriers to its adoption. Design:A qualitative study employing semi-structured interviews and a modified phenomenological approach. Setting/Subjects:Nurses who administered LDM (<10 mg/day) as the primary opioid for pain in the past three years in Hawaii and British Columbia nursing homes. Measurements:Semi-structured interviews were conducted via Zoom™ using a standardized interview guide. Interviews were recorded, transcribed verbatim, and analyzed using a qualitative description approach. Data collection continued until thematic saturation was reached. Results:Of the 11 nurse participants, most reported that LDM was effective in managing pain without major side effects, even in cases where other opioids had failed, and observed improvements in resident behavior. Four key themes emerged: initial hesitancy and the role of education, effectiveness in pain control, preferable side effect profile, and pros and cons of administration. Participants noted that LDM’s long-acting nature and liquid formulation were particularly beneficial in nursing home settings. Additionally, the use of LDM appeared to alleviate their workload by improving resident cooperation and reducing the need for frequent medication administration. Conclusions:LDM is effective and well-tolerated for pain management in nursing home residents, with minimal side effects and added benefits for resident behavior and nurse satisfaction. These findings support the need for further studies to assess LDM’s utility in nursing home settings.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".