Obesity is a Chronic Disease! A Nurse Practitioner’s Look at Raising Awareness and Addressing Self-Efficacy in Obesity Self- Management in Rural New Brunswick
Bibliographic record
Abstract
Background: The Canadian health care system is drowning in efforts to provide adequate services. One in three Canadians are considered overweight or obese. It is essential to recognize obesity as a chronic disease as it can be a leading precursor to subsequent multi-complex conditions. Attributing to this epidemic is not recognizing the importance for obesity related discussions and offering obesity structured programs. Purpose: To increase awareness obesity is a chronic disease and to raise one’s self-efficacy in obesity self-management of healthy lifestyles to improve in primary care outcomes. Methods: A Nurse practitioner led quality improvement project in Southern New Brunswick Canada; trained staff at a rural community health center in Obesity Canada’s 5AsT framework, recruited overweight or obese participants to participate in an obesity self-management online program using the evidence-based Heart and Stroke Foundation toolkit ‘Healthy Weight Action Plan' and offered weekly telephone/email support. Each weekly session focused on increasing one’s self-efficacy to motivate participants towards long-term self-management of lifestyle changes. Results: A paired t test showed a significant improvement in the pre and post intervention scores of the General Self-Efficacy scale (p=.006), the Eating Self-Efficacy Brief scale (pConclusion:Directing focus towards increasing one’s self-efficacy through structured, self-management practices, along with telephone support can improve health outcomes, effectively help address the obesity epidemic and its health care related complications. Nurse practitioners are ideal professionals to link evidence-based practices to clinical settings.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".