Weight bias and health care utilization: a scoping review
Bibliographic record
Abstract
AIM: The purpose of this scoping review was to explore the evidence on how perceptions and/or experiences of weight bias in primary health care influence engagement with and utilization of health care services by individuals with obesity. BACKGROUND: Prior studies have found discrepancies in the use of health care services by individuals living with obesity; a greater body mass index has been associated with decreased health care utilization, and weight bias has been identified as a major barrier to engagement with health services. METHODS: PubMed was searched from January 2000 to July 2017. Four reviewers independently selected 21 studies examining perceptions of weight bias and its impact on engagement with primary health care services. FINDINGS: A thematic analysis was conducted on the 21 studies that were included in this scoping review. The following 10 themes were identified: contemptuous, patronizing, and disrespectful treatment, lack of training, ambivalence, attribution of all health issues to excess weight, assumptions about weight gain, barriers to health care utilization, expectation of differential health care treatment, low trust and poor communication, avoidance or delay of health services, and 'doctor shopping'. Overall, our scoping review reveals how perceptions and/or experiences of weight bias from primary care health professionals negatively influence patient engagement with primary health care services.
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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 | 0.026 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".