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Record W7133068589

Student and supervisor experiences of health student service learning placements in rural communities

2024· article· en· W7133068589 on OpenAlexfundno aff
Elsie (Elmien); id_orcid 0000-0001-7965-6245 de Klerk, Elise; id_orcid 0000-0001-5896-1304 Ryan, Melissa; id_orcid 0000-0001-7088-5826 Nott, Elyce Green, Rebecca; id_orcid 0000-0003-2272-4694 Barry

Bibliographic record

VenueCharles Sturt University Research Output (CRO) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State UniversityFlinders UniversityUniversity of TorontoUniversity of SurreyUniversity of WaterlooCurtin University of TechnologyUniversity of the Sunshine CoastUniversity of New South WalesAuckland University of Technology, New ZealandUniversity of WollongongUniversity of Waikato
KeywordsService-learningFeelingSupervisorService (business)Experiential learningRural areaRural healthCommunity health
DOInot available

Abstract

fetched live from OpenAlex

Rural health work-integrated learning exposes students to the unique features of rural professional practice and provides opportunities to improve students’ work-readiness. Service learning placements delivered in rural settings seek to address the dual goals of student learning and meeting community identified needs. This research aimed to evaluate the experiences of students and supervisors who were involved in service learning placements in various rural and regional communities across a range of educational and health settings. Thirty-eight participants completed an online survey, reporting high levels of satisfaction with this placement format. Students experienced a strong sense of belonging within the host organisation, felt welcomed, and engaged in organizational and community activities. Supervisors universally reported feeling well supported. Ongoing attention to supervisory confidence, particularly when supervisors are unfamiliar with the service learning placement format is indicated, along with the need to develop student awareness of and access to interprofessional learning opportunities.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.129
GPT teacher head0.405
Teacher spread0.276 · 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 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
Published2024
Admission routes1
Has abstractyes

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