The health social work competency rating scale: development of a tool for education and practice
Bibliographic record
Abstract
Integrating contextual competency frameworks into health social work education and practice can bolster student training and staff supervision strategies. This article describes the iterative development of a Health Social Work Competency Rating Scale (HSWCRS), generated using a competency framework tested through simulation and an iterative research process with healthcare social workers. A modified Delphi method consisting of an e-mail questionnaire, two discussion meetings, and two rounds of classroom-based testing were employed to develop and refine the scale with the participation of clinicians, students, and researchers. The HSWCRS is designed to convey the core competencies required for single-session social work consultations in a healthcare setting by assessing formal elements (such as introduction, validation, cultural inclusiveness, client centredness), use of self (such as self-awareness, positionality), and an overall assessment of knowledge and skills. This scale adds to the competency-based education literature in social work by offering a guide to assess and measure key healthcare social work competencies in a range of educational environments, and has the potential to guide practice in educational and practice settings.
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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".