Har du förstått? En systematisk litteraturöversikt om hälsolitteracitet i primärvården
Bibliographic record
Abstract
Introduction: Health Literacy (HL) means the ability to understand, apply and actively seek information related to the individuals’ health/illness. Previous research has shown that there are major differences in health that can be linked to different levels of HL, and that the knowledge situation around HL is low in Europe compared with USA/Canada/Asia. Communication between healthcare professionals and patients is not always optimal because the patient does not understand everything conveyed by healthcare professionals. It is therefore important to find out how staff works with HL in primary care. Aim: To compile research on methods aimed at assessing and strengthening patients' health capacity in primary care Method: A systematic literature review was chosen to investigate HL. A large number of searches were conducted in scientific databases, resulting in eleven articles being included in the result. Results: Five methods used by health professionals were identified; “Teach Back”, “Bring a Friend”, “Layman Terms”, “Ask”, and “Gut Feeling”. Conclusion: The methods described cannot be used for all patients. Which method used needs to be assessed to fit each individual patient. The methods sometimes need to be combined or exchanged for another method. To better understand which method fits best, a measuring instrument for assessing patients' HL levels could be used. Further research from Europe and the Nordic Region about measuring instruments and methods for practical work with HL are needed in order to increase patient centering and reduce community costs.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".