A Novel Approach for Developing Efficient and Convenient Short Assessments to Approximate a Long Assessment
Bibliographic record
Abstract
This paper describes a novel Long to Short approach that uses machine learning to develop efficient and convenient short assessments to approximate a long assessment. This approach is applicable to any assessments used to assess people's behaviors, opinions, attitudes, mental and physical states, traits, aptitudes, abilities, and mastery of a subject matter. We demonstrated the Long to Short approach on the Depression Anxiety Stress Scale (DASS-42) for assessing anxiety levels in adults. We first obtained data for the original assessment from a large sample of participants. We then derived the total scores from participants' responses to all items of the long assessment as the ground truths. Next, we used feature selection techniques to select participants' responses to a subset of items of the long assessment to predict the ground truths accurately. We then trained machine learning models that uses the minimal number of items needed to achieve the prediction accuracy similar to that when the responses to all items of the whole long assessment are used. We generated all possible combinations of minimal number of items to create multiple short assessments of similar predictive accuracies for use if the short assessment is to be done repeatedly. Finally, we implemented the short anxiety assessments in a web application for convenient use with any future participant of the assessment.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".