The Effects of Organizational Flexibility on Nurse Utilization and Vacancy Statistics in Ontario Hospitals
Bibliographic record
Abstract
Social and economic changes in industrial societies during the past quarter-century encouraged organizations to develop greater flexibility in their employment systems in order to adapt to organizational restructuring and labour market shifts (Kallenberg 2003). During the 1990s this trend became evident in healthcare organizations. Before healthcare restructuring, employment in the acute hospital sector was more stable, with higher levels of full-time staff. However, in the downsizing era, employers favoured more flexible, contingent workforces (Zeytinoglu 1999). As healthcare systems evolved, staffing patterns became more chaotic and predicting staffing requirements more complex. Increased use of casual and part-time staff, overtime and agency nurses, as well as alterations in skills mix, masked vacancy counts and thus rendered this measurement of nursing demand increasingly difficult. This study explores flexible nurse staffing practices and demonstrates how data such as nurse vacancy statistics, considered in isolation from nurse utilization information, are inaccurate indicators of nursing demand and nurse shortage. It develops an algorithm that provides a standard methodology for improved monitoring and management of nurse utilization data and better quantification of vacancy statistics. Use of standard methodology promotes more accurate measurement of nurse utilization and shortage. Furthermore, it provides a solid base for improved nursing workforce planning, production and management.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".