The Development, Validity, and Responsiveness of a Patient-Centred Outcome Measurement Tool for Evaluating Integrative Medicine Interventions
Bibliographic record
Abstract
Background: The paper sets out the development, validity, and responsiveness of the Integrative Medicine Treatment Evaluation Form (IMTEF), which has been designed to measure the effects of complementary and integrative therapy (CIT) interventions in cancer and palliative care (PC) patients in a National Health Service (NHS) hospital setting. Treatment evaluation is essential for ensuring safety and quality of services, for meeting NHS governance requirements. It also helps to add to the evidence base for complementary and integrative therapies through collecting data about treatments. Methods: A number of different Patient Reported Outcome Measures (PROMs) tools were reviewed in order to design the IMTEF, which details questions that captures both quantitative and qualitative data. The IMTEF was reviewed by patients and a range of health care practitioners. Results: IMTEF's validity is supported by feedback from health care practitioners and patients, by its ability to detect different degrees of change in relation to change scores, and by its correlations with Visual Analog Scale (VAS) scores. Conclusion: The IMTEF can be used to assess the effects of therapeutic bodywork and CITs when many of the patients do not have the capacity or the time to answer many questions, and when therapists do not know in advance the number of treatments that patients will be able to receive. Because of the way it is structured, it can also assess the effects after a number of sessions.
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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 | 0.152 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".