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Record W2898776189 · doi:10.3148/69.1.2008.32

<i>Gaining Entry-level Clinical Competence</i>Outside of the Acute Care Setting

2008· article· en· W2898776189 on OpenAlexaffvenue
Daphne Lordly, Janette Taper

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2008
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsInternshipCompetence (human resources)Acute careNursingMedicineClinical PracticeEntry LevelMedical educationHealth carePsychologySocial psychology

Abstract

fetched live from OpenAlex

Traditionally, an emphasis has been placed on dietetic interns' attainment of entry-level clinical competence in acute care facilities. The perceived risks and benefits of acquiring entry-level clinical competence within long-term and acute care clinical environments were examined. The study included a purposive sample of recent graduates and dietitians (n=14) involved in an integrated internship program. Study subjects participated in in-depth individual interviews. Data were thematically analyzed with the support of data management software QSR N6. Perceived risks and benefits were associated with receiving clinical training exclusively in either environment; risks in one area surfaced as benefits in the other. Themes that emerged included philosophy of care, approach to practice, working environment, depth and breadth of experience, relationships (both client and professional), practice outcomes, employment opportunities, and attitude. Entry-level clinical competence is achievable in both acute and long-term care environments; however, attention must be paid to identified risks. Interns who consider gaining clinical competence exclusively in one area can reduce risks and better position themselves for employment in either practice area by incorporating an affiliation in the other area into their internship program.

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.315
GPT teacher head0.539
Teacher spread0.224 · 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.

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

Citations8
Published2008
Admission routes2
Has abstractyes

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