Child welfare worker perspectives on documentation and case recording practices in Canada: A mixed-methods study protocol
Bibliographic record
Abstract
In health care and child welfare, clinical records and case notes serve multiple functions. When records are aggregated and processed to create administrative data, they can be analyzed and used to inform policy development and decision-making. To be useful, such data should be complete, accurate, and recorded in a standardized way. However, sources of bias and error can impact the quality of administrative data. During the development of national child welfare data in Canada, child welfare sector partners expressed concerns about the accuracy and completeness of data about children and families. This protocol describes a study that seeks to answer two questions: 1) What individual and institutional factors influence how client data is recorded by child welfare workers in Canada? 2) What data quality issues are created through documentation and case recording practices that may impact the use of clinical case management system data for public health statistics? In this protocol, we describe an exploratory mixed methods study that involves an online survey, interviews with a purposive sample of child welfare workers, and a document review of case recording guidelines. To be eligible for the study, participants must have worked at a child welfare agency or department with clinical documentation responsibilities as a part of their job. We will use descriptive statistics to analyze the survey data and thematic analysis to analyze the qualitative data. This study will help uncover strengths, limitations, and possible sources of bias created through case recording and documentation practices in child welfare. Study results will be shared through presentations to interest holders and will inform the further development of national child welfare data in Canada.
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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 | 0.103 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".