The Society for Integrative Oncology Practice Recommendations for online consultation and treatment during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: The Society for Integrative Oncology (SIO) Online Task Force was created in response to the challenges facing continuity of integrative oncology care resulting from the COVID-19 pandemic. The Task Force set out to guide integrative oncology practitioners in providing effective and safe online consultations and treatments for quality-of-life-concerns and symptom management. Online treatments include manual, acupuncture, movement, mind-body, herbal, and expressive art therapies. METHODS: The SIO Online Practice Recommendations employed a four-phase consensus process: (1) literature review and discussion among an international panel of SIO members, identifying key elements essential in an integrative oncology visit; (2) development, testing, and refinement of a questionnaire defining challenges and strategies; (3) refinement input from integrative oncology experts from 19 countries; and (4) SIO Executive Committee review identifying the most high-priority challenges and strategies. RESULTS: The SIO Online Practice Recommendations address ten challenges, providing practical suggestions for online treatment/consultation. These include overcoming unfamiliarity, addressing resistance among patients and healthcare practitioners to online consultation/treatment, exploring ethical and medical-legal aspects, solving technological issues, preparing the online treatment setting, starting the online treatment session, maintaining effective communication, promoting specific treatment effects, involving the caregiver, concluding the session, and ensuring continuity of care. CONCLUSIONS: The SIO Online Practice Recommendations are relevant for ensuring continuity of care beyond the present pandemic. They can be implemented for patients with limited accessibility to integrative oncology treatments due to geographic constraints, financial difficulties, physical disability, or an unsupportive caregiver. These recommendations require further study in practice 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 | 0.049 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.018 |
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".