The perpetual journal : managing workloads in child welfare
Bibliographic record
Abstract
For decades, the subject of workload has been at the forefront of issues on the minds of child protection workers, politicians and policy makers across North America. Many public inquiries, inquests and numerous task forces, such as the Ontario Child Mortality Task Force, have produced reports that have identified unmanageable workloads as a contributing factor in the reduction of quality service to children and families (National Union¡ 1998). In many instances this reduction in quality of service has had tragic consequences for children in care. This practicum report describes and identifies, through a literature review, a series of focus groups and interjurisdictional surveys, workload methodologies used within various jurisdictions and disciplines. This report also recommends a workload reduction strategy that may allow for the reasonable allocation of caseloads that in turn, lends itself to a level of service delivery that is reflective of the needs of the children and families involved, as well as reflective of the needs of frontline workers in terms of manageable workloads.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".