Ontario COVID-19 assessment centre practices in patient counselling, education, and follow-up care: A provincial survey
Bibliographic record
Abstract
Background: In March 2020, COVID-19 assessment centres were launched across the province of Ontario to facilitate COVID-19 testing outside of emergency departments. We aimed to study the degree to which assessment centres provide education and follow-up care for patients with suspected COVID-19. Methods: We conducted an online survey of Ontario COVID-19 assessment centre directors between September 15 and October 15, 2020. The primary outcomes studied were the types of educational modalities employed and information conveyed, methods and frequency of test result communication, and any follow-up care that was offered. Survey respondents were also asked to provide descriptions of barriers to patient education and test communication. Results: A total of 56 directors (representing 73 assessment centres) completed the survey. The most frequent educational modalities employed were educational handouts (92%), direct in-person counselling (89%), and referral to website (72%). Seventy-one percent of respondents indicated patients with positive test results would be notified, and 61% of respondents indicated that follow-up care would be offered. The most frequently reported barriers to patient education were insufficient time and high volume of tests, while the most frequently reported barriers to communication of test results were difficulty accessing online health portals and high volume of tests. Conclusion: The ability of many assessment centres to provide patient education is limited by both individual patient and system-level factors. Assessment centres may benefit from standardization of educational materials, improved accessibility to test results for patients in marginalized groups, and virtual pathways to facilitate additional counselling and care for individuals who test positive.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".