Influencing Health Equity: Role of the Effective Consumer Scale in Measuring Skills and Abilities in a Middle Income Country
Bibliographic record
Abstract
The 2008 World Health Report emphasizes the need for patient-centered primary care service delivery models in which patients are equal partners in the planning and management of their health. It is argued that this involvement will lead to improved management of disease, improved health outcomes and patient satisfaction, better informed decision-making, increased compliance with healthcare decisions, and better resource utilization. This article investigates the domains captured by the Effective Consumer Scale (EC-17) in relation to vulnerable population groups that experience health inequity. Particular focus is paid to the domain of health literacy as an area fundamental to patients' involvement in managing their condition and negotiating the healthcare system. In examining the possible influence of Outcome Measures in Rheumatology Clinical Trials (OMERACT) on health equity, we used the recent translation and validation of the EC-17 scale into Spanish and tested Argentina as an example. Future plans to use the EC-17 with vulnerable groups include formal collaboration and needs assessment with the community to tailor an intervention to meet its needs in a culturally relevant manner. Some systematic reviews have questioned whether interventions to improve effective consumer skills are appropriate in vulnerable populations. We propose that these populations may have the most to gain from such interventions since they might be expected to have relatively lower skills and health literacy than other groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".