Operationalizing care in a hospital-based respirology clinic during a global pandemic
Bibliographic record
Abstract
The novel coronavirus (SARS-CoV-2) pandemic has forced clinicians across Canada to abruptly adapt to a new reality of reduced contact with patients in ambulatory care settings. With minimal lead time, ambulatory clinics in Canada were required to adopt aggressive infection prevention and control measures while facing shortages of personal protective equipment and to enact strategies to urgently reorganize patient care. These adaptations included the conversion of ambulatory clinics to telephone or virtual care platforms.In 2015, the Public Health Agency of Canada (PHAC) published pandemic influenza preparedness guidelines highlighting the importance of pandemic preparedness planning in primary and ambulatory care settings, including clinic continuity planning and provisions for non in-person care. Despite these recommendations, hospital-based ambulatory clinics lacked formal strategic plans to adapt patient care processes during the SARS-CoV-2 outbreak. Thus, in a short time frame, clinics individually adapted their own patient care processes. This creates the potential for gaps in quality of medical care for patients across Canada.In this report, we describe the evolution and operationalization of care planning in our hospital-based respirology clinic, highlight the challenges faced, and make recommendations for respirology clinic adaptations based on available guidance. This process may be used as a foundation to guide future policy, discussion and guidelines for hospital-based respiratory care to ensure optimal preparedness during the next inevitable respiratory viral pandemic or possible worsening of the current pandemic.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".