The prospective role of the primary health care nurse practitioner in the prevention and treatment of type 2 diabetes in youth
Bibliographic record
Abstract
Background: This paper examines the literature to explore the multidimensional role of primary health care nurse practitioners (PHC NPs) in type 2 diabetes prevention and management in Canadian youth within the elements of the chronic care model (CCM). Methods: Searches were conducted in 7 electronic databases and key articles and grey literature were also reviewed. Citations were screened for the following inclusion criteria: written in English; involved children or youth 11 years of age or older; and at risk or diagnosed with type 2 diabetes. Results: One thousand and one hundred sixty nine citations were found; 1,113 were excluded through title and abstract screening. A total of 56 full text manuscripts were assessed for inclusion criteria, yielding 2 that met the key question. Both studies included assessing participants’ lifestyle choices as an outcome. The first study assessed prevalence and remission of obesity; whereas the second study measured participant body mass index (BMI). Both studies reported improvements in lifestyle choices with the involvement of a PHC NP. Conclusions: PHC NP’s greatest value and impact currently lies within the community element of the CCM. Additional research examining the PHC NP’s role within the remaining elements of the CCM will demonstrate their impact in targeting type 2 diabetes prevention and management in Canadian youth.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".