Context and Work Attitudes as Antecedents of Change-Oriented Citizenship in Care Aides
Bibliographic record
Abstract
Health care is notoriously slow at adapting to new evidence. Health care organizations have increasingly promoted quality improvement programs as a solution to this issue, which rely on health professionals to propose, select and implement improvement ideas–all of which are proactive, extra-role behaviors which challenge the status quo. These change-oriented organizational citizenship behaviors (OCB) have been the subject of increasing research interest; however, they have not been studied in health care aides (HCA), who form the backbone of the long-term care workforce by providing functional assistance for our aging parents and grand-parents. We examined the antecedents of change-oriented OCB using hierarchical linear modeling (HLM) in a sample of 3,954 HCA working on 329 units in 91 facilities in western Canada. Guided by Ajzen’s Theory of Planned Behavior, we specifically examined the direct effects of various dimensions of the work environment on change-oriented OCB, as well as the mediating role of work attitudes such as job satisfaction, work engagement and psychological empowerment. We found that two dimensions of the latter–impact and self-determination–are most proximally related to change-oriented OCB and partially mediated positive effects of a supportive climate for innovation and informal exchange amongst employees on change-oriented OCB.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".