Expert understandings of supervision as a means to strengthen the social service workforce: results from a global Delphi study
Bibliographic record
Abstract
Learning on how effective social work supervision can strengthen the social service workforce is especially limited in low- and middle-income countries. To address this gap, this paper draws from a global study examining practices and approaches to effectively strengthen the social service workforce. Using a Delphi consensus methodology, the study provided a highly structured means to distil key lessons learned by experts across a range of practice and geographical settings. Over three phases, 43 global experts identified and rated the most effective practices and approaches to strengthen the social service workforce. The findings specific to supervision indicate that most experts strongly agree that access to quality supervision is important. There is also agreement related to the ways in which supervision should be carried out including: individual and group supervision, roleplaying, constructive feedback on practice, and flexibility in the supervisor–supervisee relationship. However, there is still indecision as to whether supervision should be non-hierarchical and egalitarian or, alternatively, directive and regulative. Finally, there was disagreement as to whether supervision should be incentivized. The diversity of participants’ examples suggests that the concept of ‘supervision’ is likely to be subject to highly localized variations that will challenge attempts at creating universally applicable paradigms.
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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 | 0.085 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".