A cross-sectional population survey of physicians in Alberta, Canada about a novel provincial contact tracing smartphone app
Bibliographic record
Abstract
BACKGROUND: The Canadian province of Alberta released the ABTraceTogether smartphone app in May 2020 to assist in contact tracing during the SARS-CoV-2 pandemic. Public engagement with this public health tool has been low, limiting the effectiveness of the intervention. This study examines physician knowledge of the app and practice patterns in relation to the app. METHODS: We conducted a cross-sectional self-administered online English language survey of physicians and medical students in Alberta, Canada. The survey link was sent to all registered members of the College of Physicians and Surgeons of Alberta and was distributed by other provincial physician organizations and health zone leaders. RESULTS: The survey received 317 responses. 96% of participants were aware of the app but only 27% had recommended the app to patients. The most common reason provided for not downloading or recommending the app was that participants had security concerns about the app. 23% of participants indicated they did not believe they had a responsibility to recommend the app to others. CONCLUSIONS: Our study provides insights into participants' knowledge and beliefs about the ABTraceTogether app. This information may be valuable to public health officials who wish to engage physicians in future public health campaigns.
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 | 0.002 | 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".