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Record W2501813269 · doi:10.1080/10904018.2016.1173815

Systematic Interviewing Microskills and Neuroscience: Developing Bridges between the Fields of Communication and Counseling Psychology

2016· article· en· W2501813269 on OpenAlexaff
Allen E. Ivey, Thomas Daniels

Bibliographic record

VenueInternational Journal of Listening · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsActive listeningMulticulturalismPsychologyInterviewField (mathematics)Point (geometry)Engineering ethicsCognitive sciencePedagogyPsychotherapistSociology

Abstract

fetched live from OpenAlex

Originating in 1966-68, Microcounseling was the first video-based listening program and its purposes and methods overlap with the communications field. This article presents history, research, and present applications in multiple fields nationally and internationally. Recent neuroscience and neurobiology research is presented with the important point—“counseling and communication change the brain and encourage the development of new neural networks.— Multicultural issues are important in the microskills framework. Over 500 databased Microcounseling studies exist with translations in 21 languages. The authors suggest that the communication studies field might wish to consider itself also a mental health profession as the teaching of listening skills and effective communication is central to well-being. Furthermore, communication studies can do much to increase multicultural understanding and cross-cultural communication.

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)0.074
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.070
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.003
Science and technology studies0.0040.006
Scholarly communication0.0030.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.387
Teacher spread0.340 · 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 designTheoretical or conceptual
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

Citations27
Published2016
Admission routes1
Has abstractyes

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