Bibliographic record
Abstract
In 2010, at the global campaign level in Hong Kong, the International Federation of Social Workers (IFSW) and the International Association of Schools of Social Work (IASSW) initiated a global agenda to promote social equalities, mental health, and other key issues. In Ghana, some social workers provide basic in-person counselling services in their community of work and make referrals to counselling psychologists when applicable. Unfortunately, the social distancing protocols of COVID-19 restricted the in-person sessions. In view of this, many practitioners transitioned to cyber counselling. Unfortunately, there is little or no empirical data on practitioners’ competencies in cyber counselling. This chapter reviews findings of our study, which investigated participants’ competencies in cyber counselling as the basis to develop a curriculum for training. The findings reveal high (94%) personal use of technologies but very low (27.6%) use of its application in cyber counselling. Building on the findings, this chapter provides a curriculum framework for social work practitioners to acquire competent skills for cyber counselling. We also review an ongoing collaboration between the authors in Ghana and Canada that seeks to introduce technological and professional competencies in cyber counselling to social work students and organise continuous professional development programmes for all social workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".