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Record W2154203785 · doi:10.1136/ebn.9.1.31

Perceived barriers and benefits were factors in decision making about colorectal screening

2006· letter· en· W2154203785 on OpenAlexaff
John L. Oliffe

Bibliographic record

VenueEvidence-Based Nursing · 2006
Typeletter
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePsychology

Abstract

fetched live from OpenAlex

Wackerbarth SB, Peters JC, Haist SA. “Do we really need all that equipment?” Factors influencing colorectal cancer screening decisions. Qual Health Res 2005;15:539–54.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What factors influence decision making about colorectal screening? Qualitative study based on the health belief model. Central Kentucky, USA. 30 people 48–55 years of age (mean age 54 y, 57% women) were recruited using a sampling frame including factors most likely to influence health related decisions: sex, ethnicity, domicile, type of health insurance, length of time with health provider, cancer history, screening history, and frequency of checkups. The sampling frame ensured inclusion of people who had and had not received colorectal screening. Data were collected in 30 minute semistructured interviews that were conducted in a location mutually agreeable to the participant and interviewer. Interviews were tape recorded and transcribed verbatim. The transcripts were independently analysed by 3 … [1]: {openurl}?query=rft.jtitle%253DQualitative%2BHealth%2BResearch%26rft.stitle%253DQual%2BHealth%2BRes%26rft.aulast%253DWackerbarth%26rft.auinit1%253DS.%2BB.%26rft.volume%253D15%26rft.issue%253D4%26rft.spage%253D539%26rft.epage%253D554%26rft.atitle%253D%2522Do%2BWe%2BReally%2BNeed%2BAll%2BThat%2BEquipment%253F%2522%253A%2BFactors%2BInfluencing%2BColorectal%2BCancer%2BScreening%2BDecisions%26rft_id%253Dinfo%253Adoi%252F10.1177%252F1049732304273759%26rft_id%253Dinfo%253Apmid%252F15761097%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1177/1049732304273759&link_type=DOI [3]: /lookup/external-ref?access_num=15761097&link_type=MED&atom=%2Febnurs%2F9%2F1%2F31.atom [4]: /lookup/external-ref?access_num=000227765500007&link_type=ISI

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)0.007
metaresearch head score (Gemma)0.043
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: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.036
GPT teacher head0.304
Teacher spread0.267 · 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
GenreCommentary

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

Citations2
Published2006
Admission routes1
Has abstractyes

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