La littératie en santé chez les personnes présentant des troubles mentaux graves
Bibliographic record
Abstract
Objectives The Quebec government's 2015-2020 Ministerial Mental-Health Action Plan aims at developing an optimal autonomy level in society and achieving an adequate use of healthcare services among individuals with severe mental disorders. Individuals presenting with such disorders frequently have other long-term conditions. In order to manage their conditions, these individuals must have access to, understand, and use health-related information. This corresponds to the concept of health literacy (HL). Although some research has been conducted on the HL of individuals with severe mental-health disorders, none has been done in the province of Quebec (Canada) and the measurement questionnaire previously used did not adequately take into account the multiple dimensions of HL. Using a multidimensional questionnaire would thereby be beneficial. Specifically, the objective herein was to describe the HL among individuals with a severe mental disorder residing in the community in the Saguenay-Lac-Saint-Jean region (Québec, Canada). Methods This quantitative research used a descriptive design. The participants were recruited from the community mental-health program at the Dolbeau-Mistassini CLSC, which is part of the Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-Saint-Jean. The participants were selected using a convenience sampling. Three questionnaires were used for data collection: 1) the French version of the Health Literacy Questionnaire (HLQ), a self-report questionnaire with 9 scales; 2) the French version of the Disease Burden Morbidity Assessment to get a portrait of the chronic diseases of participants; and 3) a standard sociodemographic questionnaire. The scores for the dimensions of the HLQ have been described with descriptive statistics. The average values of the nine QLS scales for the different participant subgroups were compared with Student's t-tests or ANOVA. Results Based on the overall HLQ results, the dimension Ability to understand health information reflected a lower level of HL. Overall, the averages for the various dimensions for the study population were all slightly lower than those previously described using the same questionnaire but with populations of the elderly or people with chronic conditions. Conclusion The results highlight the need to continue investigating the HL concept for the population segment with severe mental disorders. This study also brings out the need to adapt the health education delivered to this population so as to help such individuals better understand health-related information.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".