Diabetes coaching for individuals with type 2 diabetes: <scp>A</scp> state‐of‐the‐science review and rationale for a coaching model
Bibliographic record
Abstract
Diabetes coaching is emerging as an important role in self-management and care. The conceptualization of coaching, and how to implement and evaluate coaching has not been articulated in the literature. The aim of the study was to review the literature to: (i) identify the components of coaching using a validated framework, including the description of the role of technology; (ii) describe the implementation and evaluation measures for diabetes coaching; and (iii) propose a diabetes coaching model for future implementation. The EMBASE, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO and Cochrane Central Register of Controlled Trials databases were searched from inception to January 2015. Two evaluators independently screened and extracted data from eligible studies for descriptions of coaching. Eight trials met the selection criteria, with no consistency in the core components of coaching. However, elements noted across all studies included goal setting, diabetes knowledge acquisition, individualized care, and frequent follow-up. Only two studies leveraged technology for coaching communication purposes. Diabetes coaching is an intervention that can support the ongoing and complex needs of patients; however, implementation and evaluation strategies are limited in the literature. A diabetes coaching model is presented, derived from components identified throughout the literature with direction for implementation and evaluation approaches, and optimal integration into the healthcare system.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".