Evidence-Based Curricular Guidelines for Statistical Education in Social Work
Bibliographic record
Abstract
The types of statistical analyses used in more than 800 journal articles commonly cited by social workers were examined and comparisons of statistical analyses used in this published research were made between journal articles published in the late 1980s and early 2000. The data clearly indicate little has changed in the statistical methods used by social workers during the past 15 years. This analysis is used to suggest concrete evidence-based curricular guidelines in social work statistical education that meet the governing bodies (Council on Social Work Education and Canadian Association of Schools of Social Work) objectives, and attends to and further enhances the process of incremental statistical learning across all levels of education, with more advanced requirements with each program degree.
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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 | 0.358 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".