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Record W73752531

Evaluation of an integrated model of discharge planning: achieving quality discharges in an efficient and ethical way.

2002· article· en· W73752531 on OpenAlexaff
Donna L. Wells, Chantale LeClerc, Dorothy Craig, Douglas K. Martin, Victor W. Marshall

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDischarge planningResource planningHospital dischargeContext (archaeology)Resource (disambiguation)Ethical issuesQuality (philosophy)Process (computing)MedicinePsychologyOperations managementNursingComputer scienceEngineeringIntensive care medicineEnvironmental resource managementEnvironmental scienceEngineering ethics
DOInot available

Abstract

fetched live from OpenAlex

Discharge planning has become increasingly important in an era of shortened lengths of stay in hospital. Prior research demonstrated that discharge practices presented resource and ethical problems. This evaluation of an integrated model of discharge planning (IMDP) included an assessment of resource utilization, respect for persons during decision-making, and the impact of the model in meeting the needs of elderly patients, families, and professionals. Two case studies involving a university and a community hospital were used to illustrate the context in which discharge planning occurs. Within and cross-case analyses of the discharge-planning process for 48 patients indicated that it is possible to implement the IMDP and that participants were satisfied. Further, hospital resources were used efficiently and patients were involved in decision-making. The study represents a successful implementation of a promising approach to discharge planning.

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)0.034
metaresearch head score (Gemma)0.076
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.076
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
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 head0.808
GPT teacher head0.578
Teacher spread0.230 · 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

Citations13
Published2002
Admission routes1
Has abstractyes

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