Changing the Global Health Care Landscape—Proceedings of a “Glocal” Symposium
Bibliographic record
Abstract
BACKGROUND: This glocal (global knowledge with local action) symposium was convened by a professional therapeutic massage bodywork professional organization to bring together the fields of economics, politics, and traditional and complementary and alternative medicine (TCAM) to begin development of effective TCAM advocacy worldwide. The symposium addressed the core question, "What information will be needed to address issues that will arise as TCAM practitioners advocate for a respectful and equalfooting access to health care provision, public and private, worldwide?" PARTICIPANTS AND SETTING: The 35 international participants convened in a Victoria, Canada hotel. They were selectively invited to provide expertise in: advocacy, politics, public policy, economics, TCAM practice, integrative practice, sociology and TCAM research, education, media and language framing, psychology, and mediation. METHODS: The two-day symposium used a facilitated dialogue and knowledge-sharing design process geared to achieving group-supported recommendations. Invited panelists discussed each agenda topic, followed by facilitated discussion with the entire group. RESULTS: In general, participants agreed that advocacy from a TCAM perspective is needed. Additionally, more research should use methods with more relevance to everyday health care provision and health care costs such as effectiveness comparative trials and cost effectiveness studies. A number of specific advocacy steps were recommended. Most focused on developing local support for better access and equity regarding TCAM within local health care systems and advocacy work, which needs to both understand and engage the local TCAM practitioners and those using the TCAM services. CONCLUSIONS: The increasing awareness of TCAM and advancement toward integrative medicine-including traditional medicines and perspectives-are themes currently in development worldwide. Now is a good time for TCAM practitioners to open dialogue to develop better partnerships in health care. Such dialogue is facilitated when diverse people at the health care table understand each other's perspectives. More discussions like this, with diverse people across more disciplines, need to occur worldwide.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".