Collaborative Practices Between Multiple Disciplines in Care: How This Looks Like in Singapore
Bibliographic record
Abstract
The social care sector, by its nature, is crowded with professionals from several disciplines. Apart from social workers, doctors, teachers, and counsellors, increasingly psychiatrists, psychologists, occupational therapists, and other allied health professionals are working closely together in the space too. In the view of the fact that local research is only forthcoming as implementation is underway, this article aims to take stock and review the current situation in Singapore with regard to multidisciplinary collaborative practices in the social care space. Three sub-sectors, namely Family Services, Early Childhood Education, and School Counselling, will be the focus of this article. Drawing from practice-based experiences, policy frameworks, and other information through the presentation of notable developments in each of these sub-sectors, we highlight challenges faced in practice and offer recommendations for future efforts and research.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".