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Record W7144888160

所属施設の形態の違いによる看護師長の経営意識と病院の経営実態(第2報)

2019· article· ja· W7144888160 on OpenAlexaff
妙子 森木

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2019
Typearticle
Languageja
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsRemunerationNursing managementPhase (matter)Function (biology)Financial management
DOInot available

Abstract

fetched live from OpenAlex

Quantitative survey on 988 chief nurses were conducted about the difference in affiliated facility of the management awareness and the hospital management status.As a result, in management awareness, significant differences were seen in "awareness in reducing the average length of stay" and "awareness of point acquisition," according to the establishment.In addition, based on bed function, "awareness of bed occupancy rate," "awareness of reducing the average length of stay" and "awareness of home return rate" were markedly higher for patients in acute and recovery phase beds.As for scale, establishments with 700 beds or more had significantly higher management awareness.In management status, significant differences by establishment were seen only for "material cost rate," whereas notable differences were seen for all items under bed function.Based on the above, management awareness and subsequent actions are impacted more by bed function rather than by establishment.In other words, in acute and recovery phase beds, management awareness is raised and actions are taken to increase remuneration from hospitalization and medical treatment.In chronic phase beds, management awareness is raised and actions are taken to reduce costs.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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