Modelling the impact of changes in education requirements on nurses’ labour market outcomes
Bibliographic record
Abstract
Provinces across Canada, with the exception of Québec, have changed their Entry-to-Practice (ETP) requirements for Registered Nurses (RNs) from a diploma to a baccalaureate degree in nursing. The variation in introduction of the RN ETP requirement across Canada was used to investigate its impact on RNs’ probability of participation in the nursing labour force (relative to non-nursing employment and being ‘unemployed/out of the labour force/retired’), wages, and choice of care sector of employment. Licensed Practical Nurses (LPNs) simultaneously experienced a change in ETP requirement. LPNs’ labour market outcomes were also examined because LPNs are thought to offset changes in RNs’ labour force participation. Nurses’ outcomes were modelled using a nationally representative longitudinal dataset that captures nurses between 1996 and 2010, allowing for unobserved heterogeneity to correlate with the covariates. I found that the change in RN ETP requirement did not impact RNs’ or LPNs’ participation in the nursing labour force; however, RNs’ and LPNs’ probabilities of non-nursing employment and being ‘unemployed/out of the labour force/retired’ were impacted. The change in the RN ETP requirement did not impact RNs’ average wage and had no differential effect between diploma and baccalaureate RNs and between newly trained and experienced nurses. LPNs’ average wage across the country decreased post-changes in the RN ETP requirement. On the other hand, the change in the LPN ETP requirement did not impact RNs’ and LPNs’ average wage; however, newly trained baccalaureate RNs faced a wage decrease after changes in the LPN policy. The change in the RN ETP requirement had no effect on RNs’ and LPNs’ choice of care sector of employment. Relative to diploma RNs, baccalaureate RNs’ probability of working in the community sector increased after changes in the LPN ETP requirement. The likelihood of LPNs working in hospitals increased as their probability of working in the community decreased after the change in their ETP requirement. Jurisdictions should consider the labour market impact of changes in ETP requirement on targeted as well as non-targeted nursing categories, and the potential impact on a range of nurses’ labour market outcomes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".