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Record W2983941498 · doi:10.11124/jbisrir-d-19-00185

Teaching strategies and activities used for students’ clinical placement in residential aged care facilities

2019· article· en· W2983941498 on OpenAlexaff
Rose McCloskey, Lisa Keeping‐Burke, Cindy Donovan, Richelle Witherspoon, Jessica Eustace‐Cook, Nicholas Lignos

Bibliographic record

VenueThe JBI Database of Systematic Reviews and Implementation Reports · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAged careMedical educationMathematics educationGerontologyPsychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this scoping review is to examine and map current knowledge of teaching strategies and activities used with nursing students during clinical placements in residential aged care facilities. INTRODUCTION: Residential aged care facilities provide opportunities for nursing students to develop skills and interest in caring for older adults. Studies that address students' clinical placements in these settings highlight the benefits of and concerns with their experiences. Insight into the state of knowledge regarding teaching strategies used in residential aged care facilities could benefit nursing education programs and help to ensure student learning is maximized. INCLUSION CRITERIA: This scoping review will consider research and narrative reports on teaching activities and strategies used by nursing faculty and residential aged care facility staff in teaching nursing students. The concepts of interest include planned and intentional activities and strategies used to facilitate student learning and student clinical experiences. A clinical experience is defined as when a student enters a residential aged care facility and is assigned an individual or individuals to care for. METHODS: This scoping review will aim to locate published and unpublished literature employing a three-step search strategy. Only papers published in English from 1992 onward will be included. Data extracted from eligible papers will include details on the participants, context, strategy, activity and outcomes. Extracted data will be reported in a tabular form and presented narratively to address the review objective.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.762
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.128
GPT teacher head0.524
Teacher spread0.396 · 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 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
Published2019
Admission routes1
Has abstractyes

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