Massage Therapy and Canadians’ Health Care Needs 2020: Proceedings of a National Research Priority Setting Summit
Bibliographic record
Abstract
BACKGROUND: The health care landscape in Canada is changing rapidly as forces, such as an aging population, increasingly complex health issues and treatments, and economic pressure to reduce health care costs, bear down on the system. A cohesive national research agenda for massage therapy (MT) is needed in order to ensure maximum benefit is derived from research on treatment, health care policy, and cost effectiveness. SETTING: A one-day invitational summit was held in Toronto, Ontario to build strategic alliances among Canadian and international researchers, policy makers, and other stakeholders to help shape a national research agenda for MT. METHOD: Using a modified Delphi method, the summit organizers conducted two pre-summit surveys to ensure that time spent during the summit was relevant and productive. The summit was facilitated using the principles of Appreciative Inquiry which included a "4D" strategic planning approach (defining, discovery, dreaming, designing) and application of a SOAR framework (strengths, opportunities, aspirations, and results). PARTICIPANTS: Twenty-six researchers, policymakers, and other stakeholders actively participated in the events. RESULTS: Priority topics that massage therapists believe are important to the Canadian public, other health care providers, and policy makers and massage therapists themselves were identified. A framework for a national massage therapy (MT) research agenda, a grand vision of the future for MT research, and a 12-month action plan were developed. CONCLUSION: The summit provided an excellent opportunity for key stakeholders to come together and use their experience and knowledge of MT to develop a much-needed plan for moving the MT research and professionalization agenda forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".