Staff Nurse Commitment, Work Relationships, and Turnover Intentions
Bibliographic record
Abstract
BACKGROUND: The three-component model of organization commitment has typically been studied using a variable-centered rather than a person-centered approach, preventing a more complete understanding of how these forms of commitment are felt and expressed as a whole. OBJECTIVES: Latent profile analysis was used to identify qualitatively distinct categories or profiles of staff nurses' commitment. Then, associations of the profiles with perceived work unit relations and turnover intentions were examined. METHODS: Three hundred thirty-six registered nurses provided data on affective, normative, and continuance commitment, perceived work unit relations, and turnover intentions. Latent profile analysis of the nurses' commitment scores revealed six distinct profile groups. Work unit relations and turnover intentions were compared in the six profile-defined groups. RESULTS: Staff nurses with profiles characterized by high affective commitment and/or high normative commitment in relation to other components experienced stronger work unit relations and reported lower turnover intentions. Profiles characterized by high continuance commitment relative to other components or by low overall commitment experienced poorer work unit relations, and the turnover risk was higher. High continuance commitment in combination with high affective and normative commitment was experienced differently than high continuance commitment in combination with low affective and normative commitment. DISCUSSION: Healthcare organizations often foster commitment by using continuance commitment-enhancing strategies (e.g., offer high salaries and attractive benefits) that may inadvertently introduce behavioral risk. This work suggests the importance of changing the context in which continuance commitment occurs by strengthening the other two components.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".