Development of Classifier of Engagement in Occupation With Machine Learning (CEOML) for Quantifying Context
Bibliographic record
Abstract
In the evaluation of engagement in occupation, it is important to access the qualitative data, including the client context. However, there are no tools capable of quantifying such data. The purpose of this study is to develop a Classifier of Engagement in Occupation with Machine Learning (CEOML) that is capable of quantifying context and evaluating engagement in occupation through the application of Natural Language Processing (NLP) and to validate the model performance. A supervised machine learning approach was adopted in this study for the development of clinical artificial intelligence, and it was conducted based on the Minimum Information about Clinical Artificial Intelligence Modeling. The research object was the Twitter data comprising 1,542 tweets posted over a one-week period, beginning on April 1, 2020. Bidirectional Encoder Representations from Transformers, an NLP model, was fine-tuned to learn a dataset labeled for the status of engagement in occupation. The model performance was validated using indicators (sensitivity, specificity, positive predictive value, negative predictive value, F-measure, and area under the curve of receiver operating characteristic curve), Cohen’s weighted kappa coefficient, and the attention level of the model to the text. The CEOML demonstrated suitable model performance, on par with the Canadian Occupational Performance Measure. High interpretability of the CEOML was also confirmed based on its level of attention. The developed CEOML can quantify and classify problems of engagement in occupation based on the client context.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".