Bibliographic record
Abstract
Twenty years and two global nurse shortages ago, nursing workforce research was just emerging as a field. However, workforce participation among nurses and interest in nursing careers had swung widely for many decades before (Friss, 1994). Concerns about working conditions for nurses and difficulties in relations between nurses and other members of the healthcare team had long been recognized but were perhaps accelerated by the second wave of feminism beginning in the 1960s (Sullivan, 2002). Workforce research was arguably a response to the practical problems created by staffing shortages in the mid to late 1980s, when many hospitals went through periods where beds were closed or surgeries were cancelled to cope with staffing shortages. In many communities, nursing school enrollments dropped several years earlier when decreased job availability and declines in the perceived attractiveness of nursing careers led to an inability to cover rebounds in demand and normal attrition. By the late 1990s, when workforce research had already established some roots, developments in healthcare systems worldwide, especially deep cuts and restructuring of healthcare systems, were about to trigger yet another shortage several years down the line. A simplified interpretation of demographic trends (an ‘aging of the population’ story) told us that these earlier shortages were only the beginning of deep imbalances between supply and demand. However, the key ‘game changer’ in postmillennial nurse labor markets has been a global financial crisis that has led to delays in service expansion and restrictions in new hiring. For a variety of reasons, many nurses readying themselves for retirement have put off their plans indefinitely and prospects for new graduates have darkened: many hope this is only a short-term trend. Furthermore, smaller, quieter moves reframing the boundaries among the health professions and between groups of nurses (practical nurses and nurse practitioners are but two examples) are occurring across healthcare systems. Thus, while researchers and professional groups in many countries are retaining predictions of shortages in the long term, the aftershocks of the economic crisis, along with changes (often, but not always, cuts) in healthcare driven by demographic and fiscal realities, may well have reset the future of healthcare employment and prospects for nurses for good. Models of service delivery dominated by professional nurses having exclusive or nearexclusive responsibility for direct care in institutional settings are under fire at the same time as the domination of institution-based over community-based healthcare services appears to be reaching an end. So where next? It is time to reassess the purpose of this field. What makes the nurse workforce special? Is it the broad scale and scope of the services nurses provides? The nature of the work and its physical, emotional and intellectual demands? The historically gendered nature of nursing that has influenced politics within the profession and its interaction with groups and forces outside it? If
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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 | 0.089 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 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".