Automatic Classification of Psychosocial Concerns: From Traditional Approach to Deep Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The advent of artificial intelligence (AI) technologies presents promising prospects for analyzing short texts, significantly impacting the psychosocial health sector. The classification and categorization of texts represent arduous and time-consuming tasks, necessitating systematic automation to optimize the processing of traditional manual workflows. This paper presents a comparative study of various machine learning (ML) techniques in natural language processing (NLP). These techniques, designed to replace manual data categorization effectively, primarily rely on traditional algorithms such as K-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost), as well as deep learning approaches, including fine-tuning, SetFit, and few-shot learning based on transformers. A detailed analysis of different evaluation metrics revealed that the SetFit approach, integrating the sentence-transformer model, outperformed the best traditional models, with an average accuracy of 70.74% compared to 68.69 % achieved by the SVM model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it