Impacts of COVID-19 on Health Care Workers: Infection Prevention and Control, 2020 [Canada]
Bibliographic record
Abstract
The Impacts of COVID-19 on Health Care Workers: Infection Prevention and Control (ICHCWIPC) is a crowdsource initiative that collected information related to job type and setting, training and information on personal protective equipment (PPE) and infection prevention and control (IPC) practices and protocols, use and access to PPE, and personal health. It also includes general demographic questions. In the context of this product, the term crowdsourcing refers to the process of collecting information via an online questionnaire. Open advertising was used to obtain participants who chose to self-select by completing the questionnaire. As such, the crowdsourcing data was collected through a completely non-probabilistic approach which does not involve a random selection of participants like other traditional Statistics Canada surveys. Therefore, results pertain only to the participants and cannot be used to draw conclusions about the larger population of health care workers and those working in a health care setting. The targeted participants to this crowdsource initiative were health care workers and those working in a health care setting living in the ten provinces and three territories. This includes people who provided health care services directly to individuals (e.g. physicians, nurses, massage therapists, dentists, dietitians), provided technical support to medical staff (e.g. receptionists, technicians), or who provided support services in a health care setting (e.g. cleaning and food services staff, security).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".