Specialized Clinics and Health Care Professional Resources for Post–COVID-19 Condition in Canada
Bibliographic record
Abstract
This Environmental Scan aims to provide an overview of the current range and scope of both specialty clinics and available resources for health care professionals about post–COVID-19 condition in Canada. This scan was informed through a limited literature search and a survey completed by targeted contacts across Canada. Based on the survey responses, specialized clinics for post–COVID-19 condition have been established or are in development in Alberta, British Columbia, New Brunswick, Ontario, and Quebec. These clinics exist in various forms and range in their structural characteristics, operational characteristics, and stage of program development and quality improvement activities. At the time the survey was administered, there were no specialized clinics located in Manitoba, Newfoundland and Labrador, Northwest Territories, Prince Edward Island, Saskatchewan, and Yukon. These jurisdictions are at various stages in their approach to addressing post–COVID-19 condition, ranging from prevention efforts to the discussion of and planning for the implementation of clinics in their own jurisdiction. Based on the results of the literature search, there are a variety of resources that have been developed to improve the education, awareness, and training of health care professionals about post–COVID-19 condition. Referral pathways, tools for symptom screening and patient management, and educational resources, such as webinars, are among the most common. Resources about the development of clinics or models of care for post–COVID-19 condition have also been created. There is a gap in the jurisdictional representation from some provinces and territories for specialty clinics for post–COVID-19 condition. In addition, this Environmental Scan is not exhaustive and does not necessarily capture all existing clinics in each jurisdiction nor does it provide a comprehensive list of all available resources for health care professionals.
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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".