Health Care Consumer Shopping Behaviors and Sentiment: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Although some health care market reforms seek to better engage consumers in purchasing health care services, health consumer behavior remains poorly understood. OBJECTIVE: This study aimed to characterize the behaviors and sentiment of consumers who attempt to shop for health care services. METHODS: We used a semistructured interview guide based on grounded theory and standard qualitative research methods to examine components of a typical shopping process in a sample size of 54 insured adults. All interviews were systematically coded to capture consumer behaviors, barriers to shopping behavior, and sentiments associated with these experiences. RESULTS: Participants most commonly described determining and evaluating options, seeking value, and assessing or evaluating value. In total, 83% (45/54) of participants described engaging in negotiations regarding health care purchasing. The degree of positive sentiment expressed in the interview was positively correlated with identifying and determining the health plan, provider, or treatment options; making the decision to purchase; and evaluating the decision to purchase. Conversely, negative sentiment was correlated with seeking value and making the decision to buy. CONCLUSIONS: Consumer shopping behaviors are prevalent in health care purchasing and can be mapped to established consumer behavior models.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".