The 5As team intervention: bridging the knowledge gap in obesity management among primary care practitioners
Bibliographic record
Abstract
BACKGROUND: Despite opportunities for didactic education on obesity management, we still observe low rates of weight management visits in our primary care setting. This paper describes the co-creation by front-line interdisciplinary health care providers and researchers of the 5As Team intervention to improve obesity prevention and management in primary care. METHODS: We describe the theoretical foundations, design, and core elements of the 5AsT intervention, and the process of eliciting practitioners' self-identified knowledge gaps to inform the curricula for the 5AsT intervention. Themes and topics were identified through facilitated group discussion and a curriculum relevant to this group of practitioners was developed and delivered in a series of 12 workshops. RESULT: The research question and approach were co-created with the clinical leadership of the PCN; the PCN committed internal resources and a practice facilitator to the effort. Practice facilitation and learning collaboratives were used in the intervention For the content, front-line providers identified 43 topics, related to 13 themes around obesity assessment and management for which they felt the need for further education and training. These needs included: cultural identity and body image, emotional and mental health, motivation, setting goals, managing expectations, weight-bias, caregiver fatigue, clinic dynamics and team-based care, greater understanding of physiology and the use of a systematic framework for obesity assessment (the "4Ms" of obesity). The content of the 12 intervention sessions were designed based on these themes. There was a strong innovation values fit with the 5AsT intervention, and providers were more comfortable with obesity management following the intervention. The 5AsT intervention, including videos, resources and tools, has been compiled for use by clinical teams and is available online at http://www.obesitynetwork.ca/5As_Team . CONCLUSIONS: Primary care interdisciplinary practitioners perceive important knowledge gaps across a wide range of topics relevant to obesity assessment and management. This description of the intervention provides important information for trial replication. The 5AsT intervention may be a useful aid for primary care teams interested to improve their knowledge of obesity prevention and management. Clinical Trials.gov (NCT01967797).
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".