Bibliographic record
Abstract
A system in crisis. We are all feeling the heavy burden of increased workloads, lack of nursing and physician staff to provide clinical care, and higher volumes of patients seeking care in emergency departments. The relief we all hoped to feel, as case numbers, hospitalisations and deaths from COVID-19 decreased, has eluded us. We feel the moral injury of the late presentations and preventable illnesses, knowing that, despite our best efforts to provide care in this crumbling system, we have not been able to live up to the standards of our training. It is an incredibly challenging time to work in healthcare, and the pressures felt in urban areas are only amplified for those of us working in the rural communities. In many regions, rural teams have had to face the difficult decision to close or limit services. While we all understand the importance of setting boundaries and a need to have some time off to be able to sustain work long term, it is harder to put into action. There is always a sense of guilt in seeing your patients at the grocery store, your child's sports practice, while spending time exercising or out to dinner with friends, knowing the emergency room is closed for the night. However, we are not the ones to blame for this. All too often physician wellness initiatives have focused on having physicians come to terms with setting boundaries and augmenting their self-care or mindfulness strategies. However, these actions are far from sufficient to sustain our rural healthcare systems. We must demand more from our mayors, MPPs/MLAs and Federal government; rural Canada deserves better. We must also engage our patients in conversations around appropriate use of system resources, preventative care, injury prevention and ongoing strategies to reduce the transmission of illness. As part of our efforts to engage patients and governments in these critical conversations, the SRPC has launched an initiative regarding Emergency Department closures: https://srpc.ca/HHR_resource. We know there is a crisis, but does everyone else? It's time we ensure they do, as this is not our burden to carry alone.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.046 | 0.043 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".