Distributed health literacy among people living with type 2 diabetes in Portugal: Defining levels of awareness and support
Bibliographic record
Abstract
This study embraces a patient-centred and narrative-oriented notion of health literacy, exploring how social networks and personal experiences constitute distributed health literacy (DHL) by mapping out health literacy mediators of each individual and how they enable self-management skills and knowledge of health conditions. Semi-structured interviews with 26 patients with type 2 diabetes were conducted in a Primary Care Center of Porto (Portugal) from October 2014 to December 2015. Data were collected based on McGill Illness Narrative Interview (MINI). Following the grounded theory, interviews were analysed as case-based and process-tracing-oriented. Three awareness narratives emerged: (i) a narrative of minimisation revealing minimal impact of diabetes in patients' lives and daily routines, resignation towards "inevitable" consequences of the diagnosis and dependence of a large network of health literacy mediators; (ii) a narrative of empathy, where patients tended to mention readjustments in their lives by following medical recommendations regarding medication without criticism and with few health literacy mediators; (iii) a narrative of disruption, with patients highlighting the huge impact of diabetes on their lives and their individual responsibility and autonomy with respect to the management of diabetes and the search for alternatives to medication, relying on a very restrictive network of mediators. Exploring meanings given to diagnosis, identifying health mediators and analysing the structure of social networks can contribute to understand the distributed nature of health literacy. Assessing DHL can assist health professionals and those providing care in the community in promoting health literacy and providing models for a more patient-centred health system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".