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Record W2582730465

The development and preliminary testing of an instrument to measure the perceived economic burden of a subtype of arrhythmogenic right ventricular cardiomyopathy (ARVC) on patients and families in Newfoundland

2016· dissertation· en· W2582730465 on OpenAlexaboutno aff
Glenn Enright

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2016
Typedissertation
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScale (ratio)Likert scalePopulationClinical psychologyGerontologyPsychologyEnvironmental healthDevelopmental psychologyGeography
DOInot available

Abstract

fetched live from OpenAlex

The goal of this thesis was to develop, construct, and validate the Perceived Economic Burden scale to quantitatively measure the burden associated with a subtype Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) in families from the island of Newfoundland. An original 76 item self-administered survey was designed using content from existing literature as well as themes from qualitative research conducted by our team and distributed to individuals of families known to be at risk for the disease. A response rate of 37.2% (n = 64) was achieved between December 2013 and May 2014. Tests for data quality, Likert scale assumptions and scale reliability were conducted and provided preliminary evidence of the psychometric properties of the final constructed perceived economic burden of ARVC scale comprising 62 items in five sections. Findings indicated that being an affected male was a significant predictor of increased perceived economic burden in the majority of economic burden measures. Affected males also reported an increased likelihood of going on disability and difficulty obtaining insurance. Affected females also had an increased perceived financial burden. Preliminary results suggest that a perceived economic burden exists within the ARVC population in Newfoundland.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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