The adoption of high involvement work practices in Canadian nursing homes
Bibliographic record
Abstract
PURPOSE: The objective of the research is to assess the degree of adoption of high-involvement nursing work practices in long-term care organizations. It seeks to determine the organizational and workplace factors that are associated with the uptake/adoption of ten selected human resource high-involvement employee work practices. DESIGN/METHODOLOGY/APPROACH: A survey questionnaire was sent to 300 long-term care organizations (nursing homes) in western Canada. Results from 125 nursing home establishments (43 percent response rate) are reported herein. FINDINGS: Of the ten high-involvement nursing work practices examined, employee suggestion and recognition systems are the most widely adopted by homes in the sample, while shared governance and incentive/merit-base pay are used by a small minority of establishments. PRACTICAL IMPLICATIONS: The uptake of high-involvement nursing work practices is not adopted in a haphazard fashion. Their uptake is variously associated with a number of establishment and workplace factors, including the presence of a supportive and enabling workplace culture. ORIGINALITY/VALUE: The objective of this research is to examine the extent and degree of adoption of high involvement work practices in a sample of long-term care establishments operating in the four provinces of western Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".