How Different Motivations for Making Informal Out‐Of‐Pocket Payments Vary in Their Influence on Users’ Satisfaction with Healthcare, Local and National Government, and Satisfaction with Life?
Bibliographic record
Abstract
BACKGROUND: The dominant view in the literature is that informal payments in healthcare universally are a negative phenomenon. By contrast, we theorize that the motivation healthcare users for making informal payments (IP) can be classified into three categories: (1) a cultural norm, (2) "grease the wheels" payments if users offered to pay to get better services, and (3) "sand the wheels" payments if users were asked to pay by healthcare personnel or felt that payments were expected. We further hypothesize that these three categories of payments are differently associated with a user's outcomes, namely, satisfaction with healthcare, local and national government, satisfaction with life, and satisfaction with life of children in the future. METHODS: We used microdata from the 2016 Life-in-Transition survey. Multivariate regression analysis is used to quantify relationships between these categories of payments and users' outcomes. RESULTS: Payments that are the result of cultural norms are associated with better outcomes. On the contrary, "sand the wheel" payments are associated with worse outcomes. We find no association between making "grease the wheels" payments and outcomes. CONCLUSIONS: This is the first paper which evaluates association between three different categories of informal payments with a wide range of users' outcomes on a diverse sample of countries. Focusing on informal payments in general, rather than explicitly examining specific motivations, obscures the true outcomes of making IP. It is important to distinguish between three different motivations for informal payment, namely, cultural norms, "grease the wheels," and "sand the wheels" since they have varying associations with user outcomes. From a policy making standpoint, variation in the links between different motivations for making IP and measures of satisfaction suggest that decision-makers should put their primary focus on situations where IP are explicitly asked for or are implied by the situation and that they should differentiate this from cases of gratitude payments. If such measures are not implemented, then policy makers may unintentionally ban the behaviour that is linked with increased satisfaction with healthcare, government, and life (i.e., paying gratitude).
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".