Factors influencing primary care provider referral for bariatric surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To identify barriers to and facilitators of primary care provider (PCP) referral for bariatric surgery in patients with obesity. DATA SOURCES: MEDLINE, EMBASE, and PsycINFO databases were searched and reference lists of included articles were screened to identify additional relevant articles. Two reviewers independently reviewed citations and full-text articles, and appraised the quality of the included articles using the Critical Appraisal Skills Programme Tool Qualitative Checklist and the Appraisal Tool for Cross-Sectional Studies. They extracted data on the study characteristics and the barriers to and facilitators of PCP referral for bariatric surgery. Appraisal discrepancies were resolved through consensus among authors. STUDY SELECTION: Overall, 882 citations were identified and 18 articles were then selected for this review. SYNTHESIS: Barriers included fear of surgery complications and side effects, cost, lack of availability, perception that surgery is a quick fix or a last resort, and prior negative experiences. Facilitators included direct requests from patients, patient motivation, previously failed weight-loss interventions, and obesity-related comorbidities. Those PCPs who were knowledgeable about the risks and benefits of bariatric surgery were more likely to refer their patients. CONCLUSION: Education and continuing professional development programs regarding bariatric surgery are needed to improve PCP knowledge and capacity to manage patients with obesity. Also, educating the general public on obesity, weight management, and available treatment options can empower patients and families to manage their weight and pursue evidence-informed treatments.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it