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Record W7133063905

Examining the relationship between therapist traits and empathy on the development of therapist empathic skills

2008· dissertation· W7133063905 on OpenAlexfundno aff
Laura Gollino

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmpathyInterpersonal Reactivity IndexPerspective-takingTraitPerspective (graphical)Emotional contagionEmpathic concernInterpersonal communication
DOInot available

Abstract

fetched live from OpenAlex

This study investigated relationships between empathy and the therapist traits of perspective taking, susceptibility to emotional contagion, and valuing and acceptance of emotional experiences, comparing masters- (N = 14) and doctoral-level (N = 5) counselling trainees. Three measures of state empathy were used: the BLRI self-report forms for client and therapist (Barrett-Lennard, 1962), and the observer-rated Measure of Expressed Empathy (Watson Prosser, 2002). Participants also completed the Interpersonal Reactivity Index (Davis, 1983), the Emotional Contagion Scale (Doherty, 1997), and the Scales for Emotional Experiencing (Behr Becker, 2004). The study's purpose was to further current understanding of the relationship between training and empathy. A significant effect of training on state but not trait empathy was found, as well as support for relationships among training, state empathy, and the therapist traits of perspective taking and emotional experiencing. Results suggest training helps trainees develop and manage affective reactions related to empathy.

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)0.002
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.372
Teacher spread0.274 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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