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Record W3165269154 · doi:10.3138/cpp.2020-151

Does the Profit Motive Matter? COVID-19 Prevention and Management in Ontario Long-Term-Care Homes

2021· article· en· W3165269154 on OpenAlexaffvenueabout
Kristen Pue, Daniel Westlake, Alix Jansen

Bibliographic record

VenueCanadian Public Policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of SaskatchewanUniversity of TorontoCarleton University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Long-term careTerm (time)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BusinessProfit (economics)PsychologyEconomicsMedicineVirologyPsychiatryMicroeconomics

Abstract

fetched live from OpenAlex

We introduce evidence that for-profit long-term-care providers are associated with less successful outcomes in coronavirus disease 2019 outbreak management. We introduce two sets of theoretical arguments that predict variation in service quality by provider type: those that deal with the institution of contracting (innovative competition vs. erosive competition) and those that address organizational features of for-profit, non-profit, and government actors (profit seeking, cross-subsidization, and future investment). We contextualize these arguments through a discussion of how contracting operates in Ontario long-term care. That discussion leads us to exclude the institutional arguments while retaining the arguments about organizational features as our three hypotheses. Using outbreak data as of February 2021, we find that government-run long-term-care homes surpassed for-profit and non-profit homes in outbreak management, consistent with an earlier finding from Stall et al. (2020). Non-profit homes outperform for-profit homes but are outperformed by government-run homes. These results are consistent with the expectations derived from two theoretical arguments-profit seeking and cross-subsidization-and inconsistent with a third-capacity for future investment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.039
GPT teacher head0.369
Teacher spread0.330 · 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.

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".

Quick stats

Citations21
Published2021
Admission routes3
Has abstractyes

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