Perceived barriers and benefits were factors in decision making about colorectal screening
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".