Bibliographic record
Abstract
In Canada, nurses have known about the chronic shortage of nurses for years; the pandemic has just opened the floodgates. For the authors, the current nursing crisis and the accompanying response have led to flashbacks of the early 2000s, when extensive advocacy work took place to prevent a looming nursing crisis. In the key reports reviewed in this paper, the statement "lack of respect for nursing" has echoed over and over and over again and continues to be heard today throughout social media. Based on nurses' voices, meaningful respect starts with nurse leaders and administrators recognizing nurses' education, knowledge, values and experience; seeking and listening to nurses' voices and input on decisions affecting nursing; and striving for quality practice environments with reasonable workloads, adequate supplies and resources. While long-term planning must take place to correct this, there is no easy fix and no single strategy to turn the situation around quickly. Short-term strategies to relieve nurses' feelings of disrespect are a good place to start to retain nurses and stop the bleeding. It is time to work with all the nurses to find ground-level strategies to assure a sustainable and healthy nursing workforce for today and tomorrow. In this paper, the authors provide an overview of the meaning of respect both generally and from the nurses' perspective using the literature from the past 20 years. The authors then outline several implications for nurse leaders and administrators that are relevant today.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".