MétaCan
Menu
Back to cohort
Record W3116671167 · doi:10.7759/cureus.12247

Addressing Social Context in Health Provider and Senior Communication Training: What Can We Learn From Communication Accommodation Theory?

2020· article· en· W3116671167 on OpenAlexaff
Beheshta Momand, Adam Dubrowski

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsMedicineAccommodationConverseContext (archaeology)Health careVariety (cybernetics)CognitionMental healthNursingApplied psychologyMedical educationGerontologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

The ability to communicate enables people to share information, thoughts, and concerns with others in a certain time and place. Communication plays a fundamental role across a variety of institutions. However, the stakes are exceptionally high in primary health care (PHC). Poor communication in PHC increases the patient's risk of medication errors, patient injury, delay in treatment, and/or death. Effective communication is especially critical when health providers communicate with seniors because aging is partly responsible for physical, mental, and social/emotional changes. Studies have suggested that simulations are an effective means to train health providers in the development/enhanced communication skills; however, current educational programs focus on physical and cognitive aspects of aging. This editorial highlights possible contributions from the communication accommodation theory (CAT) to structure a communication training strategy that may help to improve healthcare providers' ability to converse and connect with the vulnerable older population and address their social and emotional well-being.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.457
GPT teacher head0.459
Teacher spread0.002 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicPatient-Provider Communication in HealthcareFrench-language works237,207