Bibliographic record
Abstract
Social innovation can involve reexamining traditional perspectives from new angles. This study illustrates how replicating a published study can provide new insights into understanding symptoms of depression and anxiety among medical students. The primary aim of this study is to reanalyze the dataset published by Santander-Hernandez et al. (2022), employing data mining techniques to expand upon the findings of the original study. In Santander-Hernandez et al. (2022), a questionnaire was administered to 371 medical students in Peru covering various aspects, such as smartphone dependence, insomnia, depressive symptoms, anxiety symptoms, body mass index, suicidal ideation, and other demographic factors. The current study analyzed this dataset using a data mining approach, specifically a classification tree. The results of the data mining, with a prediction accuracy of 78.04%, indicate that insomnia, suicidal ideation, body mass index, and study year may be associated with symptoms of depression and anxiety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".