THE RELATIONSHIP OF WORK STATUS (IN)CONGRUENCY WITH PERCEIVED EMPLOYER COMMITMENT AND COMMITMENT OF COMMUNITY HEALTH NURSES
Bibliographic record
Abstract
Using a social exchange framework, we examined the relationship of work status (in)congruency with organization-specific and job-specific aspects of perceived employer commitment and with nurses ’ commitment to their organization, to their job, and to their profession. The participants were 1,230 female nurses employed in community health agencies in Canada. Part-time nurses preferring full-time work perceived greater job insecurity and lack of access to development opportunities, but reported the highest level of commitment to their organization and profession. Full-time nurses preferring part-time work perceived less organizational support, reported lowest level of meaning empowerment, affective, normative, and professional commitment, but reported greatest continuance commitment. Work status congruency occurs when a person’s current work status (full-time or part-time) matches their preference for work status (prefers to work full-time or prefers to work part-time). Work status incongruency occurs when a person’s work status is inconsistent with the person’s preferred work status, i.e., the person is working full-time but prefers to work part-time or the person is working parttime but prefers to work full-time. Work status (in)congruency has been shown to be significantly related to employee attitudes and well-being including job satisfaction (Armstrong-Stassen, Al-Ma’aitah,
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".