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Record W2778578957 · doi:10.1186/s13104-017-3098-3

Cross-cultural survey development: The Colon Cancer Screening Behaviors Survey for South Asian populations

2017· article· en· W2778578957 on OpenAlexafffund
Joanne Crawford, Dorcas Beaton, Farah Ahmad, Arlene S. Bierman

Bibliographic record

VenueBMC Research Notes · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of TorontoBrock UniversityInstitute for Work & HealthYork UniversityToronto Rehabilitation InstituteSt. Michael's Hospital
FundersBrock University
KeywordsMedicineRelevance (law)Cultural diversityUrduPsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this work was to develop a survey that considered cultural relevance and diversity of South Asian populations, with the aim of describing or predicting factors that influence colorectal cancer screening intention and adherence. The scientifically rigorous approach for survey development informed the final phase of an exploratory mixed method study. This initial survey was later cross-culturally translated and adapted into the Urdu language, and thereafter, items were cognitively tested for conceptual relevance among South Asian immigrants. RESULTS: The initial development of the Colon Cancer Screening Behaviours Survey for South Asian populations was completed using a number of steps. Development involved: the identification of key concepts and conceptual model; literature search for candidate measures and critical appraisal; and, expert consultation to select relevant measures. Five published surveys included measures that covered concepts relevant to South Asians and colorectal cancer screening behaviours. However, measures from these surveys missed content that emerged through parallel field work with South Asians, and additions were required along with item modifications. In the final stage, cross-cultural translation and adaptation into Urdu, and cognitive testing were completed. Future research will require an examination of proposed relationships, and psychometric testing of measures in the survey.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.000
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 head0.573
GPT teacher head0.552
Teacher spread0.021 · 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.

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

Citations6
Published2017
Admission routes2
Has abstractyes

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