A Parallel-Group, Randomized Trial Examining Impact of Colposcopy Results Delivery by a Nurse Liaison on Patient-Reported Outcomes and Adherence
Bibliographic record
Abstract
OBJECTIVES: Cervical cancer is on the rise in Canada. Addressing patient anxiety and improving patient understanding of colposcopy and results may improve adherence. This randomized controlled trial examined the impact of colposcopy results delivery by a Nurse Liaison versus the referring primary care provider (PCP) on patient anxiety, and secondary outcomes including patient satisfaction, knowledge of diagnosis, and 9-month adherence to follow-up. METHODS: Patients ≥18 years old presenting for initial appointment at the study colposcopy clinic were randomized 1:1 to an intervention group (Nurse Liaison) versus a control group (PCP). After receiving colposcopy results, participants completed online measures of anxiety (State-Trait Anxiety Inventory), health care satisfaction scales (Patient Satisfaction Questionnaire-18, Health Anxiety Inventory, Visit-Specific Satisfaction Questionnaire-9), self-reported colposcopy diagnosis, and demographics. Chart review at 9 months assessed adherence to recommended colposcopy follow-up. Groups were compared on continuous and categorical variables, controlling for diagnosis severity and trait anxiety. RESULTS: The intervention group had significantly lower state anxiety with State-Trait Anxiety Inventory-state mean scores of 37.3 versus 40.7 in controls (P = 0.03). Intervention group participants were more likely to correctly report their diagnosis (84% vs. 66.3%, P = 0.003). Questionnaire responders were more likely to be in the intervention group and had a higher proportion of cervical intraepithelial neoplasia 2+ pathology. There were no differences in demographics, patient satisfaction, or adherence to follow-up between groups. CONCLUSIONS: Direct delivery of colposcopy results by a trained Nurse Liaison was associated with decreased patient anxiety around colposcopy results, and increased patient knowledge regarding diagnosis. This model may be considered to improve patient-centred care.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".