Exploring and comparing client perception of need and social worker perception of risk : a key to improved intervention in cases of child neglect
Bibliographic record
Abstract
Clients involved with child protection systems due to issues of neglect are known to have multiple needs. The issues that they confront are personal, situational, and social in nature. The emphasis on risk reduction in many jurisdictions within North America has meant that needs have been given less priority. The aim of the exploratory study was to gain a better understanding of both the nature of needs and risks in cases of child neglect in Ontario, as well as the similarities and differences in the views of clients and child protection workers. It is posited that through the acquisition of knowledge in those areas, that improvements can be made in assessing and planning, in creating agreed upon expectations about the objectives of intervention, and in developing a better balance between the addressing of needs and risks. For the study, an instrument was designed to measure client perceptions of their problems and needs. It was compared with workers' perceptions of risk as contained in the risk assessment instrument completed by all child protection workers in Ontario. The Client Perception of Problems and Needs Scale was administered to 77 parents receiving services from Family and Children's Services of Renfrew County due to concerns about child neglect. The finding that participants felt their needs were greatest in dealing with issues of stress, child behaviour and mental health issues, and in coping with socio-economic disadvantage was congruent with the few studies that have been conducted on the perceptions of child protection clients about their needs and problems. The analysis of the risk assessment data provided evidence that reliance on risk reduction at the expense of needs-based approaches, is not warranted. Few similarities were found in the perceptions of clients and workers about the issues of greatest concern. However, it was surprising that few concerns emerged about the clients' living conditions, or the affective interaction between clients and their children. Finally, the study demonstrated that the participants were able to recognize their problems, used various coping strategies for dealing with them, and were able to articulate strengths and resources on which they relied.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".