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Record W4245915966 · doi:10.21203/rs.3.rs-412993/v1

Characteristics of the ideal hospitalist inpatient care program: perceptions of Canadian health system leaders

2021· preprint· en· W4245915966 on OpenAlexafffundabout
Vandad Yousefi, Elayne McIvor

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of British Columbia
FundersFraser Health Authority
KeywordsStaffingWorkloadNursingInpatient careHealth careService (business)Ideal (ethics)MedicineWork (physics)Business

Abstract

fetched live from OpenAlex

Abstract Background: Despite the growing prevalence of hospitalist programs in Canada, it is not clear what program features are deemed desirable by administrative and medical leaders who oversee them. We aimed to understand perceptions of a wide range of healthcare administrators and frontline providers about the necessary characteristics of a hospitalist service. Methods: We conducted semi-structured interviews with a range of administrators, medical leaders and frontline providers across three hospital sites in an integrated health system in Western Canada. Results: Most interviewees identified the hospitalist model as the ideal inpatient care service line, but identified a number of challenges. Interviewees identified the necessary features of an ideal hospitalist service to include considerations for program design, care and non-clinical processes, and alignment between workload and physician staffing. Conclusions: Most hospital administrators and frontline providers in our study believed the hospitalist model resulted in improvements in clinical processes and work environment.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.062
GPT teacher head0.402
Teacher spread0.340 · 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 designQualitative
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

Citations0
Published2021
Admission routes3
Has abstractyes

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