Understanding Primary Health Care Provider’s Perceptions of Using Activity Monitors: A Qualitative Study
Bibliographic record
Abstract
Context: Individuals with excess bodyweight represent a large portion of the population of Nova Scotia, Canada and cardiovascular disease is the second leading cause of death. Given that obesity is a primary risk factor for cardiovascular disease, primary care techniques targeting overweight and obesity are essential. An activity monitor (AM) is a wearable device that track’s multiple health parameters related to obesity. Primary health care providers (PHCP) are the gate keepers of health care in Nova Scotia. Their perceptions on AM are integral to the use of AM’s in primary care. Objective: Understand PHCP perceived barriers and facilitators regarding the use of AM’s with their patients in-person (i.e., clinical) and in virtual healthcare settings. Study design: Participants were interviewed individually (i.e., 20-30 min, audio-recorded, semi-structured). Setting: Rural Nova Scotia. Population studied: Five PHCP’s [i.e., 2 Registered Nurses (RN), 2 Nurse Practitioners (NP), and 1 Medical Doctor (MD)]. Outcome measures: Interview transcripts were compiled to find commonalities connected to the socioecological model which includes: individual, interpersonal, organizational, community, and public policy. Results: Individual: Technology competency is a barrier to AM implementation and older PHCP9s had less trust in the reliability and ability for AM’s to produce positive outcomes. Interpersonal: PHCP9s saw a benefit in using AM’s in healthcare and were enthusiastic that AM’s would help patients, but they believed that someone else should be responsible for their implementation and delivery within the healthcare system. Organizational: PHCP’s suggested that using software that can be easily integrated into electronic medical records to analyze patient AM data may allow more time and objective information to recommend effective prevention strategies with patients. Community: PHCP’s believed that AM’s would have good uptake among patients and would allow patients to become more responsible for their own health, lessening the burden on PHCP’s. Accessibility was also identified as significant to implementation. Policy: RN’s and NP’s identified financial resources as a potential barrier. Despite this, most felt that AM’s were worth the cost for the sake of preventative care. Conclusion: All participants identified AM’s as an innovative health tool with potential to be used in-person and in virtual healthcare 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".