Social insurance literacy: a scoping review on how to define and measure it
Bibliographic record
Abstract
Purpose Sickness insurance and workers’ compensation systems decide on peoples’ eligibility for benefits, and are commonly based on medical certificates and assessments of work ability. Systems differ in the extent to which they preserve clients’ dignity and right to fair assessments. In this article, we define a new concept for studying interactions between individuals and systems: social insurance literacy, which concerns how well people understand the different procedures and regulations in social insurance systems, and how well systems communicate with clients in order to help them understand the system.Methods The concept was defined through a scoping literature review of related concepts, a conceptual re-analysis in relation to the social insurance field, and a following workshop.Results Five related concepts were reviewed for definitions and operationalizations: health literacy, financial/economic literacy, legal capability/ability, social security literacy, and insurance literacy.Conclusions Social insurance literacy is defined as the extent to which individuals can obtain, understand and act on information in a social insurance system, related to the comprehensibility of the information provided by the system. This definition is rooted in theories from sociology, social medicine and public health. In the next step, a measure for the concept will be developed based on this review.Implications for rehabilitationSocial insurance literacy is a new concept that is based on theories in sociology, social medicine and public health.It provides conceptual orientation for analyzing factors that may influence different outcomes of peoples’ contacts with social insurance systems.The concept is of relevance for rehabilitation professionals since it focuses on how interactions between individuals and systems can influence the rehabilitation process.The study will in the next step develop a measure of social insurance literacy which will have practical applications for rehabilitation professionals.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".