Insights into nurses’ work: Exploring relationships among work attitudes and work-related behaviors
Bibliographic record
Abstract
BACKGROUND: Work attitudes have been associated with work productivity. In health care, poor work attitudes have been linked to poor performance, decreased patient safety, and quality care. Hence, the importance, ascribed in the literature, of work that clearly identifies the relationships between and among work attitudes and work behaviors linked to performance. PURPOSE: The purpose of this study is to better understand the relationships between work attitudes-perceived organizational justice, perceived organizational support (POS), affective commitment-consistently associated with a key type of performance outcome among nurses' organizational citizenship behaviors (OCBs). METHODOLOGY: A survey was developed and administered to frontline nurses working in the province of Ontario, Canada. Data analysis used path analytic techniques. RESULTS: Direct associations were identified between interpersonal justice and POS, procedural justice and POS, and POS and affective commitment to both one's supervisor and one's co-workers. Affective commitment to patients and career was directly associated with OCBs. Affective commitment to one's co-worker was directly associated with OCBs directed toward individuals, as affective commitment to one's organization was with OCBs directed toward the organization. Finally, OCBIs and OCBs were directly associated. CONCLUSIONS: Examining the relationships of these constructs in a single model is novel and provides new information regarding their complexity. Findings suggest that prior approaches to studying these relationships may have been undernuanced, and conceptualizations may have led to somewhat inaccurate conclusions regarding their associations. PRACTICE IMPLICATIONS: With limited resources, knowledge of nurse work attitudes can inform human resource practices and operational policies involving training programs in employee communication, transparency, interaction, support, and performance evaluation.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".