Bibliographic record
Abstract
Purpose The purpose of this paper is to review evidence related to the factors that influence people's understanding of health information and how miscommunication of health information can jeopardize people's health. Design/methodology/approach A literature review was conducted of English language articles, cited in major literature databases from the last 40 years, which describe factors related to comprehension of health information. A total of 93 articles were included. Findings The paper finds that health communication should take into consideration the role of the following factors on the processing and interpretation of health information: health literacy, format presentation of information, and human cognitive biases and affective/personal influences. Practical implications Health communication is a major component of health care. Every health care encounter involves exchange of information, which is intended to enhance people's knowledge in order to assist them to make an informed decision about their health care. However, the mere act of providing information does not guarantee comprehension. People's comprehension of information depends on several factors, including health literacy and numeracy skills, the format presentation of health information and human cognitive biases in the information processing and interpretation. Ineffective health communication can result in a wide range of direct and indirect health consequences including failure to understand and comply with treatment, poorer health status, increased risk of injuries, increased hospitalization, and decreased use of preventive services. Originality/value This paper provides health professionals and educators with an overview of important issues related to health communication and highlights strategies that facilitate effective communication to help people to make informed decisions about their health care.
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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".