P12 Concept analysis of health literacy: a nursing diagnosis proposal
Bibliographic record
Abstract
Introduction Health Literacy (HL) interferes on health outcomes. This phenomenon enables nurses to identify individuals who may develop required skills for adequate HL. NANDA-I Taxonomy has diagnosis concerning specific aspects of HL. For this reason, either reviewing them or developing the Insufficient HL diagnosis is necessary. Objective To identify the elements within HL concept, in order to review or develop a nursing diagnosis on HL. Methods A review, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, led the concept analysis (concept selection, aims of conceptual analysis, concept possible use, detect the defining attributes, development of a model and additional cases, detect the antecedents, consequences and empirical references). A search was conducted in the PubMed, CINAHL, Embase, and LILACS database to identify original studies on patient health literacy, regardless of their age and health care setting. Combinations of key words and controlled vocabulary were used. Results The review highlighted HL cognitive, behavioural and affective attributes. The antecedents included limited health knowledge, health policies, health determinants, inefficient decision-making, and insufficient social support. The consequences were poor information exchange; medication and treatment non-adherence, prescription mistaken, and improper control of chronic diseases. The model and additional cases were developed, and specific empirical references identified. Conclusion Based on concept analysis elements, we recommend to either create the Insufficient HL nursing diagnosis or review the diagnoses on NANDA-I.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".