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Record W4396983522 · doi:10.69520/jipe.v4i1.102

Phone visiting as a novel clinical experience for health care students during COVID-19 and beyond

2022· article· en· W4396983522 on OpenAlexaffabout
Paula Mayer, Heather Nelson, Beverlee Ziefflie, Susan Page, Deborah Norton

Bibliographic record

VenueJournal of innovation in polytechnic education. · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSaskatchewan Polytechnic
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PhonePandemicHealth careMedicinePsychologyMedical educationVirologyPolitical scienceDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, there was a need to utilize innovative clinical placements for health care students. The Saskatchewan Polytechnic Continuing Care Assistant (CCA) Program created a five-week phone visiting program to meet the clinical needs of CCA students and to assist older adults who were experiencing social isolation during the COVID-19 pandemic. Student evaluations from the project were analyzed using Braun and Clarke’s (2006) thematic analysis and resulted in three themes: building communication skills, communication as your job, and older adults as people. This program was successful in providing students the opportunity to practice communication, learn the importance of effective communication in the workplace, and view older adults from a new perspective. The phone visiting program was beneficial for both students and the older adults involved. Phone visiting programs would be a beneficial addition to health sciences programs as part of clinical or communication classes.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0060.003
Open science0.0020.011
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.057
GPT teacher head0.504
Teacher spread0.447 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

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