Reactions to a Targeted Intervention to Increase Fecal Occult Blood Testing among Average-Risk Adults Waiting for Screening Colonoscopy
Bibliographic record
Abstract
BACKGROUND: Increasing demand combined with limited capacity has resulted in long wait times for average-risk adults referred for screening colonoscopy for colorectal cancer. Management of patients on these growing wait lists is an emerging clinical issue. OBJECTIVE: To inform the content and design of a mailed targeted invitation for patients to undergo annual fecal occult blood testing (FOBT) while awaiting colonoscopy. METHODS: Focus groups (FGs) with average-risk patients on a wait list for screening colonoscopy at a high-throughput academic outpatient colonoscopy facility were conducted. During each FG session, feedback regarding a range of materials under consideration for the planned intervention was elicited using a semistructured facilitator guide. The FG sessions were recorded and transcribed verbatim, and analyzed using the constant comparative method to identify key themes. RESULTS: Findings from the three FGs (n=28) suggested that average risk patients on a wait list for screening colonoscopy would be receptive to a targeted intervention recommending they undergo FOBT while waiting. Participants indicated that the invitation to undergo FOBT was an important acknowledgement that they were on an actively managed list, and that a mechanism to ensure that they were correctly triaged while waiting was in place. Several specific suggestions to improve the design of the targeted intervention were obtained. CONCLUSIONS: Results of the present study provide useful information for developing effective strategies to manage average-risk individuals facing long wait times for screening colonoscopy.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".