Trust in Insurers and Access to Physicians: Associated Enrollee Behaviors and Changes over Time
Bibliographic record
Abstract
OBJECTIVE: Most studies of trust in the medical arena have focused on trust in physicians rather than trust in health insurers, and have been cross-sectional rather than longitudinal studies. This study examined associations among trust in a managed care insurer, trust in one's primary physician, and subsequent enrollee behaviors relating to source of care. The study also documents changes in trust in the study population following the disclosure of physician incentives. STUDY SETTING: A medium-sized (300,000 member) HMO, located in the southeastern United States. DATA COLLECTION: One to two years after baseline, we randomly resurveyed a quarter (n = 558) of the initial study population of a large intervention study designed to measure the impact of disclosing HMO financial incentives on patient trust. This follow-up study was also designed to measure the effects of trust on source of care. ANALYSES: Multivariate regression analyses of survey data examined associations between baseline levels of trust and subsequent enrollee behaviors such as using a non-PCP physician without a PCP referral, as well as changes in trust since baseline. RESULTS: High baseline insurer trust was associated with a lower probability of a patient seeking care from a non-PCP physician (OR = 0.55, 95 percent CI: 0.33, 0.91). No long-term effects of prior disclosure of financial incentives were observed. Overall, there was a slight increase in overall trust in the insurer (1.8 percent, p < .05) but no change in trust in one's primary physician. The increase in insurer trust was primarily restricted to 23 percent of the enrollees who had changed their PCPs following the baseline survey (6.6 percent, p < .01). In multivariate analyses, changing physicians was the most significant predictor of increased insurer trust (OR = 2.17, 95 percent CI: 1.37, 3.43). CONCLUSIONS. Trust in one's insurer seems to change over time more than trust in one's primary physician, and is predictive of enrollee behaviors such as seeking care from other physicians. The ability to change physicians seems to increase trust in the insurer.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".