NARRATIVE MEDICINE IN EDUCATION: EXPLORING IMPACT OF EXPRESSE DEMOTIONAL INTELLIGENCE ON MENTAL WELL-BEING OF STUDENTS THROUGH NIGERIA LITERATURE CONTENT
Bibliographic record
Abstract
This study investigated narrative medicine in education: exploring impact of expressed emotional intelligence on mental well-being of students through Nigeria literature content. The study adopted a descriptive survey design of quantitative nature. The population for this study consisted of 250 Senior Secondary school students who were offering English Literature as a subject in Lagos state. Purposive random sampling technique was used to select 250 secondary school students consisting of 115 male and 135 female students from five randomly selected public secondary school in Lagos State. Two research questions were answered and impact of literature content on expressed emotional intelligence and mental well-being of students’scale was used for data collection. The instrument is self-constructed by the researchers and it was validated using test-retest method. It had an internal consistency of 0.86. The data of the study were analyzed using percentage, bar chart, pie chart, and multiple regression analysis statistical tools to answer the research questions at 0.05 alpha level of significance. The result of the study revealed that the use of Nigeria literature content as narrative medicine in education impact positively on expressed emotional intelligence and mental well-being of secondary school students. Therefore, it was recommended that students should be encouraged to read literary works because reading literature fosters empathy by allowing individuals to step into the shoes of diverse characters and understand their struggles.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".