Bibliographic record
Abstract
Out of clutter, find simplicity.From discord, find harmony.In the middle of difficulty, lies opportunity.-Albert Einstein (Calaprice 1996) This is the final issue of the Canadian Journal of Nursing Leadership (CJNL) for 2021, and the final issue for this editor-in-chief.It is with some reluctance that I step away from this role, but I am a firm believer in knowing when your expiry date is pending.When I took over CJNL from Dorothy Pringle in 2010, the task seemed daunting -what big shoes I had to fill -and almost immediately, imposter syndrome took hold of my psyche.But clearly, I forged ahead, and more than 10 years later, I have reflected on my editorial tenure, but more about that later.The events of the past two years, and perhaps many months yet to come, may well be remembered as the greatest societal disruption of our lifetime, as citizens, nurses and leaders.Adding up to a loss of more than 5.3 million lives worldwide to date (WHO 2021), the pandemic has challenged our leadership in ways we could never have imagined.The devastating path has been indiscriminate, and the collateral damage significant, particularly within healthcare settings and among clinicians.The escalation of workplace violence and racism, the intensification of woefully inadequate staffing, impossible spans of control and unreasonable nurse-patient ratios are, perhaps, finally at a critical tipping point.The tragic failings in long-term care and intensifying nursing shortages have flagged the dire state of affairs.Tired of dealing with all of the aforementioned issues and more, nurses are leaving the profession in droves.They are justifiably choosing self-preservation over exhaustion and quitting rather than submitting to forced overtime and untenable work environments.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".