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Record W2594576665 · doi:10.1093/restud/rdaa082

The Returns to Nursing: Evidence from a Parental-Leave Program

2020· article· en· W2594576665 on OpenAlexaff
Benjamin Friedrich, Martin B. Hackmann

Bibliographic record

VenueThe Review of Economic Studies · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsKellogg's (Canada)
FundersDanmarks Grundforskningsfond
KeywordsImmigrationContext (archaeology)NursingDemographic economicsEconomic shortageMedicineEmpirical evidenceUnintended consequencesNursing shortageHealth careBusinessLabour economicsEconomicsNurse educationPolitical scienceEconomic growthGovernment (linguistics)

Abstract

fetched live from OpenAlex

Abstract In this article, we quantify the effects of nurses on health care delivery and patient health in the context of an unintended and policy-induced nurse shortage. Our empirical strategy takes advantage of a parental-leave program in Denmark, which offered any parent the opportunity to take up to one year’s absence per child aged 0–8. Combining the policy variation with administrative employer–employee match data, we document substantial program take-up among nurses, who could not be replaced on net despite public education and immigration expansion efforts to mitigate the employment effects. We find that the parental leave program reduced hospital and nursing home nurse employment by 15% and 10%, respectively. Using detailed patient health records, we find detrimental effects on hospital-care delivery as indicated by a large increase in 30-day readmission rates among acute myocardial infarction patients. We find no evidence for an increase in hospital mortality. In nursing homes, we estimate a large increase in mortality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.300
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.240
GPT teacher head0.507
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations44
Published2020
Admission routes1
Has abstractyes

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