Participant Experience of an Electronic Health Coaching Trial: A Qualitative Inquiry
Bibliographic record
Abstract
Introduction: The study objective was to investigate the experience of individuals living with Type 2 diabetes (T2DM), some of who participated in a smartphone-based health coaching intervention, and, particularly, their motivations for health behaviour change. Methods: A qualitative investigation was undertaken with subjects from a larger T2DM self-management RCT (2011-2014) at the Black Creek Community Health Centre in Toronto, Ontario. Twenty semi- structured interviews were conducted and analyzed with a thematic analytic approach to explore relevant themes. The focus was to investigate the effectiveness of 6 months of smartphone-based health coaching versus a control group who also received health coaching but without smartphone assistance. Results: Data analysis resulted in four major themes (1) Smartphone and Software described how participants used the device in relation to health behaviour change; (2) Health Coach described the relationship between clients and health coaches; (3) Overall Experience described individuals perception and experience of the intervention; and (4) Frustrations in Managing Chronic Conditions, described the challenges of T2DM management. Discussion: Findings suggest that interventions with T2DM assisted by smartphone software and health coaches actively engage individuals in improved hemoglobin A1c (HbA1c) control.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".