Consensus Recommendations to NCCIH from Research Faculty in a Transdisciplinary Academic Consortium for Complementary and Integrative Health and Medicine
Bibliographic record
Abstract
BACKGROUND: This commentary presents the most impactful, shared priorities for research investment across the licensed complementary and integrative health (CIH) disciplines according to the Academic Consortium for Complementary and Alternative Health Care (ACCAHC). These are (1) research on whole disciplines; (2) costs; and (3) building capacity within the disciplines' universities, colleges, and programs. The issue of research capacity is emphasized. DISCUSSION: ACCAHC urges expansion of investment in the development of researchers who are graduates of CIH programs, particularly those with a continued association with accredited CIH schools. To increase capacity of CIH discipline researchers, we recommend National Center for Complementary and Integrative Health (NCCIH) to (1) continue and expand R25 grants for education in evidence-based healthcare and evidence-informed practice at CIH schools; (2) work to limit researcher attrition from CIH institutions by supporting career development grants for clinicians from licensed CIH fields who are affiliated with and dedicated to continuing to work in accredited CIH schools; (3) fund additional stand-alone grants to CIH institutions that already have a strong research foundation, and collaborate with appropriate National Institutes of Health (NIH) institutes and centers to create infrastructure in these institutions; (4) stimulate higher percentages of grants to conventional centers to require or strongly encourage partnership with CIH institutions or CIH researchers based at CIH institutions, or give priority to those that do; (5) fund research conferences, workshops, and symposia developed through accredited CIH schools, including those that explore best methods for studying the impact of whole disciplines; and (6) following the present NIH policy of giving priority to new researchers, we urge NCCIH to give a marginal benefit to grant applications from CIH clinician-researchers at CIH academic/research institutions, to acknowledge that CIH concepts require specialized expertise to translate to conventional perspectives. SUMMARY: We commend NCCIH for its previous efforts to support high-quality research in the CIH disciplines. As NCCIH develops its 2016-2020 strategic plan, these recommendations to prioritize research based on whole disciplines, encourage collection of outcome data related to costs, and further support capacity-building within CIH institutions remain relevant and are a strategic use of funds that can benefit the nation's health.
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 | 0.259 | 0.485 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.022 | 0.032 |
| Research integrity | 0.080 | 0.064 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".