Mentorship Programs within a Network to Build Research Literacy & Capacity in Complementary & Alternative Medicine (CAM) Practitioners
Bibliographic record
Abstract
Background: A lack of research literacy and capacity has been identified as a key barrier to research in complementary and alternative medicine (CAM). Networks may enable multidisciplinary collaboration between academic researchers and CAM practitioners and provide opportunities for mentoring and capacity building. Little is known about how mentoring can operate within a network and what its potential is as a strategy to increase research literacy and capacity among CAM practitioners. Purpose: To explore how mentoring within a network can be used to build research literacy and capacity amongst CAM practitioners. Methodology: Qualitative method was used for data collection and content analysis. Participants were individuals with knowledge and/or experience in networks and/or mentoring, as well as CAM practitioners. Results: Background: A lack of research literacy and capacity has been identified as a key barrier to research in complementary and alternative medicine (CAM). Networks may enable multidisciplinary collaboration between academic researchers and CAM practitioners and provide opportunities for mentoring and capacity building. Little is known about how mentoring can operate within a network and what its potential is as a strategy to increase research literacy and capacity among CAM practitioners. Purpose: To explore how mentoring within a network can be used to build research literacy and capacity amongst CAM practitioners. Methodology: Qualitative method was used for data collection and content analysis. Participants were individuals with knowledge and/or experience in networks and/or mentoring, as well as CAM practitioners. Results: Some major categories derived from the data were: 1) A network must clearly define the role and goals of mentoring. 2) An infrastructure to support and maintain the mentoring relationship should be in place. 3) A number of barriers and enablers for successful mentoring within a network were addressed. 4) Some issues specific to CAM practitioners ability to conduct research were raised. Conclusion: Mentoring was seen as a potentially useful strategy. Ideas for mentoring were suggested, including cooperative models, job shadowing, perceptorship and role modeling. Innovative models were discussed.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".