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Record W2923357913 · doi:10.1002/jgc4.1111

Impact of the physical environment on patient outcomes of genetic counseling: An exploratory study

2019· article· en· W2923357913 on OpenAlexafffund
Emily Morris, Jacob Best, Angela Inglis, Jehannine Austin

Bibliographic record

VenueJournal of Genetic Counseling · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of British Columbia
FundersCanada Research Chairs
KeywordsMedicineGenetic counselingNaturalistic observationExploratory researchClinical psychologyFamily medicinePhysical therapyPsychology

Abstract

fetched live from OpenAlex

The psychology literature shows that the physical space in which counseling sessions are conducted influences outcomes of the interaction. However, this phenomenon has not been quantitatively explored in genetic counseling (GC). Through retrospective review of naturalistic data from a psychiatric GC clinic (where data on patient outcomes are routinely tracked from pre- to 1 month post-appointment using the Genetic Counseling Outcome Scale (GCOS, empowerment) and the Illness Management Self Efficacy Scale (IMSES), we tested the hypotheses that patients seen in comfortably furnished counseling (C-type) rooms would have greater increases in (a) empowerment and (b) self-efficacy after GC than patients seen in medically oriented (M-type) rooms. We matched each patient with complete GCOS and/or IMSES who was seen in a C-type room between February 2012 and December 2017 to four M-type room controls where possible. We used t tests to compare change in outcome scale scores between groups. There were no significant differences in change in scores between patients seen in M-type (GCOS n = 84, IMSES n = 56) and C-type rooms (GCOS n = 22, IMSES n = 18) (p = 0.241, d = 0.26, and p = 0.602, d = 0.14, respectively). The effect sizes we demonstrate allow estimation of sample size calculations for the design of future prospective studies.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.141
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.298
Teacher spread0.277 · 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.

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

Citations7
Published2019
Admission routes2
Has abstractyes

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