Bibliographic record
Abstract
This qualitative study explored the extent to which the Covid-19 pandemic has impacted Canadian hospitals and emergency department (ED) wait times. A purposive sampling procedure was used for this study to conduct a content analysis on a sample of 50 of the most recent and relevant comments that included reactions, personal experiences, and possible solutions towards ED wait times from a CBC News article. A coding procedure examined any frequent themes and subcategories in the comments. Results showed six consistently present categorical themes: Wait Times, Shortage of Workers, Underfunded Healthcare System, Unrelated Covid-19 Symptoms, Avoidance, and Solutions. Furthermore, additional subcategories were determined from the themes. This study analyzes the intense backlogs of surgical cases and waiting rooms, resulting in adverse patient outcomes. Additionally, this study explores the underfunded and understaffed healthcare system, the stresses healthcare workers face daily, and possible solutions to mend this broken healthcare system.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".