Factors influencing primary care provider referral for bariatric surgery
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.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".