Demystifying diabetes health coaching: A scoping review unveiling the ‘who’ and ‘where’ of health coaching for adults with type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a complex chronic condition that requires ongoing self-management. Diabetes health coaching interventions provide personalized healthcare programming to address physical and psychosocial aspects of diabetes self-management. AIMS: This scoping review aims to explore the contexts and settings of diabetes health coaching interventions for adults with T2D, using the RE-AIM framework. METHODS: A search was completed in MEDLINE, PsycINFO, Emcare, Embase and Cochrane. Included citations described adults with exclusively T2D who had received a health coaching intervention. Citations were excluded if they focused on any other types of diabetes or diabetes prevention. RESULTS: A total of 3418 records were identified through database and manual searches, with 29 citations selected for data extraction. Most health coaching interventions were delivered by health professionals, many employed lay health workers and a few included peer coaches. While many health coaching interventions were delivered remotely, in-person intervention settings were distributed among primary care, community health settings and non-healthcare sites. CONCLUSION: The findings of this review suggest that diabetes health coaching may be implemented by a variety of providers in different settings. Further research is required to standardize training and implementation of health coaching and evaluate its long-term effectiveness.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".