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Record W4285406526 · doi:10.12927/hcpol.2022.26850

Inspection Reports: The Canary in the Coal Mine

2022· article· fr· W4285406526 on OpenAlexaffvenueabout
Mary Crea‐Arsenio, Andrea Baumann, Vicki Smith

Bibliographic record

VenueHealthcare policy · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsNeglectDescriptive statisticsForensic engineeringPsychologyMedicineNursingEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

Neglect in the Ontario long-term care (LTC) sector is defined under section 5 of O. Reg. 79/10 of the Long-Term Care Homes Act, 2007. Allegations are monitored and investigated via inspections. Using an exploratory descriptive design, we analyzed reports of neglect in LTC homes from 2019 to 2020. The majority were in response to critical incidents, followed by complaints from family members or staff. Thematic analysis revealed four areas of neglect: (1) failure to provide treatment; (2) failure to provide care; (3) failure to attend to or assist residents; and (4) failure to investigate allegations. Study findings demonstrate that an accountability framework that includes consequences for institutions is needed.

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)0.005
metaresearch head score (Gemma)0.027
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 score0.578
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.374
Teacher spread0.325 · 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".

Quick stats

Citations3
Published2022
Admission routes3
Has abstractyes

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