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Record W2522976065 · doi:10.5430/jha.v5n6p63

Adopt-a-Hospital Project: An instructional tool for hospital administration

2016· article· en· W2522976065 on OpenAlexvenueno aff
Asa B. Wilson

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipPreceptorGraduation (instrument)Experiential learningCurriculumMedical educationAcute careContext (archaeology)MedicineHealth careNursingAdministration (probate law)PsychologyPedagogyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Backgournd: More often than not, university health administration curriculums are generic and are not foundational to a specific career track. This is especially true in relation to the transition from graduation to a hospital administration career progression. The overarching question is, “How does one prepare themselves for senior leadership in an acute care hospital setting?”Objective: A semester-long assignment – Adopt-a-Hospital Project – is discussed in the context of a healthcare finance course as tool for preparing students to think administratively regarding hospital operations. This Project is presented as an academic foundation preparing students for the required semester-long internship placement in an acute care hospital.Results: The Project-Internship sequence has, over a four-year period, demonstrated its value as an academic and experiential learning bridge from the academy to the world of work. Informal, qualitative findings are discussed in terms of a future quantitative study incorporating: (1) preceptor surveys, (2) intern surveys, and (3) focus group feedback.Conclusions: The Project-Internship sequence fosters a link between academic content and experiential learning in an acute care hospital – thereby augmenting one’s post-graduation readiness to pursue a hospital administration career track.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.325
Teacher spread0.311 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2016
Admission routes1
Has abstractyes

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