Determinants of Inadequate Health Literacy Among Faculty of Medical Technical Sciences Students in Albania: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Health literacy (HL) refers to people's ability to find, understand, and use health information in order to make appropriate health decisions. Health literacy among students is important so that tomorrow's health professionals can deliver high-quality health care and enhance patient education and communication. OBJECTIVE: In this context, the aim of this study was to assess the HL level of Faculty of Medical Technical Sciences (FMTS) students in order to shed light on this underresearched topic in Albanian settings. METHODS: A cross-sectional study involving 193 FMTS students of various study branches (nursing, midwifery, physiotherapy, and laboratory technician) was carried out during June 22-29, 2022, on the premises of the FMTS Faculty in Tirana, Albania. The international European Health Literacy Survey Questionnaire (HLS-EU-Q) standardized questionnaire, validated in Albanian, was used to collect information about FMTS students' general HL through a face-to-face interview. Basic sociodemographic information was collected as well. Binary logistic regression was used to assess the factors associated with inadequate, problematic, or limited HL. RESULTS: The mean level of general HL was 37.2 (on a scale from 0 [minimum HL] to 50 [maximal HL]). About one-quarter of FMTS students had inadequate (9/193, 4.7%) or problematic (38/193, 19.7%) HL, 51.3% (99/193) had sufficient HL, and 24.4% (47/193) had excellent HL. The prevalence of limited HL (inadequate and problematic HL) was higher among male than female students (6/12, 50% vs 41/181, 22.6%, respectively) and those with lower social and economic status. Upon adjusting for confounding effects, the only factor significantly increasing the likelihood of limited HL was male gender (odds ratio 8.13, 95% CI 1.68-39.39). Findings suggested that low social and economic status also increased the likelihood of limited HL, but such associations did not reach statistical significance. CONCLUSIONS: To our knowledge, this is the first study exploring the level of HL and its associated factors among FMTS students in Albania. The prevalence of limited HL was relatively high among FMTS students. There is a need for targeted interventions to increase the HL of nursing and midwifery students, such as the inclusion of HL subjects in the nursing curriculum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".