Factors Influencing Female Registered Nurses' Work Behavior
Bibliographic record
Abstract
OBJECTIVE: To analyze factors that are related to whether registered nurses (RNs) work (WK) or do not work (NW) in nursing; and if the RN works, whether she works full- (FT) or part-time (PT). DATA SOURCES: Secondary data from National Sample Survey of Registered Nurses 2000 (NSSRN), the InterStudy Competitive Edge Part III Regional Market Analysis (2001), and the Area Resource File (2002). STUDY DESIGN: Using a cross-sectional design we tested the relationship between WK or NW and FT or PT; and demographic, job-related, and metropolitan statistical area (MSA)-level variables. DATA COLLECTION/EXTRACTION METHODS: We combined the data sources noted above to produce the analytic sample of 25,471 female RNs. PRINCIPAL FINDINGS: Working in nursing is not independent of working FT or PT. Age (55 and older), other family income, and prior other work experience in health care are negatively related to working as an RN. The wage is not related to working as an RN, but negatively influences FT work. Age, children, minority status, student status, employment status, other income, and some job settings have a negative impact on working FT. Previous health care work has a positive effect on whether married RNs worked. Married RNs who are more dissatisfied are less likely to work FT. A greater number of market-level factors influence FT/PT than WK/NW behavior. CONCLUSIONS: An important contribution of this study is demonstrating that MSA-level variables influence RN work behavior. The market environment seems to have little effect on whether a nurse works, but is influential on how much the nurse works, and has differential effects on married versus single nurses.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".