Patients’ perceptions on non‐specific effects of acupuncture: Qualitative comparison between responders and non‐responders
Bibliographic record
Abstract
BACKGROUND: Non-specific effect of acupuncture constitutes part of the overall effect generated via clinical encounter beyond needle insertion and stimulation. It is unclear how responders and non-responders of acupuncture experience non-specific effects differently. We aimed to compare their experiences in a nested qualitative study embedded in an acupuncture randomized trial on functional dyspepsia. METHODS: Purposive sampling was used to capture experience of responders (n=15) and non-responders (n=15) to acupuncture via individual in-depth interviews. Design and analysis followed a framework analysis approach, with reference to an existing model on acupuncture non-specific effects. Themes emerging outside of this model were purposefully explored. RESULTS: Responders had a more trusting relationship with acupuncturist in response to their expression of empathy. In turn they were more actively engaged in lifestyle modifications and dietary advice offered by acupuncturists. Non-responders were not satisfied with the level of reassurance regarding acupuncture safety. They were also expecting more peer support from fellow participants, regarded that as an empowerment process for initiating and sustaining lifestyle changes. CONCLUSIONS: Our results highlighted key differences in acupuncture non-specific effect components experienced by responders and non-responders. Positive non-specific effects contributing to overall benefits could be enhanced by emphasizing on empathy expression from acupuncturists, trust-building, offering appropriate explanations on safety, and organizing patient support groups. Further research on the relative importance of each component is warranted.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".