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Record W2890833721 · doi:10.23889/ijpds.v3i4.965

Attending Nurse Practitioners in Long-Term Care Homes Evaluation

2018· article· en· W2890833721 on OpenAlexaboutno aff
Danielle Fearon, Refik Saskin, Lisa Ishiguro, Erin Graves

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLong-term careHealth careEmergency departmentChristian ministryHospital bedFamily medicineMedical emergencyNursingEmergency medicine

Abstract

fetched live from OpenAlex

IntroductionIn 2014, the Ontario Ministry of Health and Long-Term Care (MOHLTC) announced funding for 75 nurse practitioners (NPs) over three years in long-term care (LTC) homes. This evaluation was approved by ICES’ Applied Health Research Question (AHRQ) team, a portfolio which answers questions from stakeholders having impact on healthcare policy. Objectives and ApproachThe purpose of this project is to evaluate the impact of the first thirty NPs hired. Changes will be evaluated using key outcome measures of resident care (e.g., early hospital discharge, emergency room bed days) identified through a literature review conducted by the MOHLTC. LTC home residents were identified using all individuals with claims in OHIP during the 2016-17 fiscal year with a location of a LTC home. LTC homes with a hired NP were considered to be cases and all other LTC homes were considered to be controls. ResultsFor part one of this evaluation, case and control LTC homes were stratified by bed size, Case Mix Index, rurality and Local Health Integration Network. Hospitalization records and emergency visits (from Discharge Abstract Database and National Ambulatory Care Reporting System) were determined for LTCH residents 6 months before and after the NP hire date of October 1, 2016. Overall, the rate of hospital admissions (per 100 residents) increased by 3.44% (8.51% to 11.94%) following the NP hire date; whereas, the rate of hospital admissions increased by 2.29% (6.55% to 8.83%) among controls. Following the NP hire date, the rate of emergency department visits also increased by 3.15% among cases (16.62% to 19.77%) in comparison to a 2.31% increase among controls (12.55% to 14.86%). Conclusion/ImplicationsThe findings from this evaluation will inform further implementation strategies of the NP program and guide decision-making of future funding opportunities. In summary, the results will inform policies to strengthen care of LTC homes and improve the quality of care of residents.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.153
GPT teacher head0.585
Teacher spread0.432 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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