Expert understandings of supervision as a means to strengthen the social service workforce: results from a global Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it