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Record W2081107257 · doi:10.1007/s10897-014-9713-8

Evaluation of a Clinical Genetics Service – A Quality Initiative

2014· article· en· W2081107257 on OpenAlexaff
Alison M. Elliott, Bernard N. Chodirker, Patricia Bocangel, Aizeddin Mhanni

Bibliographic record

VenueJournal of Genetic Counseling · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsGeneticistGenetic counselingConfidentialityMedical geneticsReferralMedicineGenetic testingTest (biology)Family medicineQuality (philosophy)Service (business)Meaning (existential)Human geneticsMedical educationPsychologyNursingGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Paper-based surveys are an effective means of evaluating the quality of a clinical service. As part of ongoing quality improvement initiatives within our Genetics Program, new patients were invited to participate in a paper-based survey. Issues related to the quality of counseling based on educational/informational aspects (e.g. whether testing was explained fully, testing options, the meaning of normal/abnormal testing), competency, respect and nondirectiveness of counseling in addition to clinical environment/setting were evaluated. Data related to demographics, discipline seen within the program and whether the patient was seen by a physician or genetic counselor were also captured. Five hundred questionnaires were distributed. One hundred and forty-seven questionnaires were returned, with a response rate of 29.4 %. The majority of patients seen were prenatal (pregnant) patients and comprised a heterogeneous group including those seen for advanced maternal age and abnormal maternal serum screening. Overall, 98.6 % of respondents felt their appointment in genetics was a positive experience. Issues related to confidentiality, pros and cons of testing, meaning of an abnormal test result and time allotted for decision making were significantly different in some disciplines between genetic counselor and geneticist. However, when controlling for referral indication, these differences lost significance with the exception of issues relating to confidentiality and perceived time allotted to organize thoughts and questions. This survey provided valuable information to allow for improvement in the quality of the provision of service.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.547
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.111
GPT teacher head0.433
Teacher spread0.322 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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