International Expert Panel Consensus Guidelines for Structure and Delivery of Qigong Exercise for Cancer Care Programming
Bibliographic record
Abstract
Integrative oncology, including Qigong, is a relatively new concept in modern healthcare. Evidence of benefits of Qigong in cancer survivors is emerging. As such, several cancer centers, world-wide, have introduced Qigong as part of integrative medicine within supportive cancer care programming. Qigong exercise programming content and quality varies among institutions due to lack of standard guidelines and, at present, relies solely on the instructor’s skills, knowledge, personal preferences and clinical experience. Development of consensus guidelines recommending the basic structure and delivery of Qigong programming in cancer care can potentiate quality assurance and reduce risk of harm. This applied qualitative research utilized a modified Delphi approach to formulate consensus guidelines. Guidelines were developed through discussions among an international expert panel (N = 13) with representation from Australia, Canada, Ireland, and the United States. Panel communication was predominantly conducted by email and occurred from November 2016 through February 2017. Expert panel work resulted in the generation of a work product: Qigong in Cancer Care Guidelines: A Working Paper including: (a) Consensus Guidelines for structure and delivery of Qigong exercise for Cancer care programming; (b) Consensus guidelines for instructor competence for teaching Qigong exercise for cancer care classes; (c) Screening tool for safe participation in Qigong exercise; (d) Class participant instructions for maintaining safety during Qigong exercise; and (e) Advice from the field. Generation of these resources is the first step in establishing recommendations for ‘best practice’ in the area of Qigong for cancer care programming.
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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 | 0.237 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".