Patient perspectives on tablet-based technology to collect risk factor information in primary care
Bibliographic record
Abstract
BACKGROUND: Primary care provides an opportunity to introduce prevention strategies and identify risk behaviours. Algorithmic information technology such as the Risk Factor Identification Tool (RFIT) can support primary care counseling. This study explores the integration of the tablet-based RFIT in primary care clinics to support exploration of patient risk factor information. METHODS: Qualitative study to explore patients' perspectives of RFIT. RFIT was implemented in two primary care clinics in Manitoba, Canada. There were 207 patients who completed RFIT, offered to them by eight family physicians. We conducted one-on-one patient interviews with 86 patients to capture the patient's perspective. Responses were coded and categorized into five common themes. RESULTS: RFIT had a completion rate of 86%. Clinic staff reported that very few patients declined the use of RFIT or required assistance to use the tablet. Patients reported that the tablet-based RFIT provided a user-friendly interface that enabled self-reflection while in the waiting room. Patients discussed the impact of RFIT on the patient-provider interaction, utility for the clinician, their concerns and suggested improvements for RFIT. Among the patients who used RFIT 12.1% smoked, 21.2% felt their diet could be improved, 9.3% reported high alcohol consumption, 56.4% reported less than 150 min of PA a week, and 8.2% lived in poverty. CONCLUSION: RFIT is a user-friendly tool for the collection of patient risk behaviour information. RFIT is particularly useful for patients lacking continuity in the care they receive. Information technology can promote self-reflection while providing useful information to the primary care clinician. When combined with practical tools and resources RFIT can assist in the reduction of risk behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".