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Record W3041676959 · doi:10.11124/jbisrir-d-19-00334

Teaching strategies and activities to enhance students’ clinical placement in residential aged care facilities: a scoping review

2020· review· en· W3041676959 on OpenAlexaff
Rose McCloskey, Lisa Keeping‐Burke, Cindy Donovan, Jessica Eustace‐Cook, Richelle Witherspoon, Nicholas Lignos

Bibliographic record

VenueJBI Evidence Synthesis · 2020
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSaint John Regional HospitalUniversity of New Brunswick
Fundersnot available
KeywordsMedical educationGerontologyMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this scoping review was to examine teaching strategies and activities used in nursing students' clinical placement in residential aged care facilities. INTRODUCTION: Population aging necessitates that nursing curricula ensure student interest and commitment to working with older adults. While searching for suitable clinical placements that provide students with opportunities to care for older adults, nursing programs have turned to residential aged care facilities. Studies show that carefully planned placement in these environments supports students' needs and offers rich learning possibilities. INCLUSION CRITERIA: This review examined intentional teaching strategies and activities used during student placement in residential aged care facilities, and considered research and textual papers on the subject. The strategies and activities included those that took place prior to, during, or after the experience. METHODS: The review included qualitative and quantitative research reports as well as text and opinion papers. Only research reports and papers published in English from 1992 to August 2019 were included. The databases searched were: CINAHL (EBSCO), MEDLINE (Ovid), Academic Search Premier (EBSCO), Embase (Elsevier), ERIC (EBSCO), ProQuest Dissertations and Theses, and Google (with advanced search strategies). Two independent reviewers screened citations for inclusion while a third reviewer resolved discrepancies. A table was developed for data extraction to record data relating to the review objective. Specific data extracted included the details on research design, geographical location, year of publication, description of the teaching strategy or activity. RESULTS: Of the 84 research reports and papers that were eligible for full-text review, only 25 (30%) were included in the final set. Sixteen papers were research reports including a variety of qualitative, quantitative, and mixed method designs. The remaining nine were textual papers and included frameworks, descriptions, and evaluations of a teaching strategy or activity. Most research reports and papers identified more than one strategy and/or activity used concurrently. The use of care staff as student mentors and facility orientation for students were the two most common strategies and activities reported. CONCLUSION: A range of teaching approaches during clinical placements in residential aged care facilities was revealed. These approaches targeted students, staff of aged care facilities, and nursing faculty. Collaborative efforts between aged care facilities and educational institutions allowed for the pooling of resources and the delivery of teaching approaches to students and the engagement of care staff. Many of the approaches were co-designed by educational programs and residential aged care facilities. The number of approaches that used more than one teaching strategy and/or activity reflects an appreciation for the importance of student placements and the complexities of aged care facilities. A lack of longitudinal or evaluative research highlights a gap in the literature. There is a need for further work to understand and evaluate the long-term effects and benefits of teaching strategies and activities used to enhance students' clinical placements in resident aged care facilities.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.107
GPT teacher head0.541
Teacher spread0.434 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2020
Admission routes1
Has abstractyes

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