Travelling meanings: meaningful measures at a child protective service in the Netherlands
Bibliographic record
Abstract
Background: In recent years, a child protection service in the Netherlands has concentrated on developing a collection of instruments, questionnaires and assessment tools that are family-centred and aid professionals in their decision-making, while also offering insightful data for the organization's monitoring and evaluation. They have dubbed these newly created instruments “meaningful measures”. The organisation’s aim is to lower the bureaucratic burden on professionals and to align organisational metrics and indicators with the work being done on the ground. These professionals are tasked with planning and coordinating care for families and to guide them as they move through a multi-institutional trajectory. This study aims to gain an understanding of how professionals are working with these instruments in providing good care and how the meanings of the instruments travel and are conceptualised as boundary objects. Methods: A qualitative study in which 34 professionals were interviewed regarding their perspectives on the importance and function of "meaningful measures" for themselves, families and their organisations. Respondents were chosen at random and all levels of seniority and experience were included. The research team conducted a thematic analysis of the data with two rounds of open coding. Findings: In this study we found that the instruments interact with different layers of actors and their governance structures involved, and that they are nested within wider networks of standards and institutional practices. The instruments help professionals to gain insight into and overview of the family’s needs and situation and to reflect upon their own actions and those of the families. In team meetings, professionals argue their scoring in a discussion. The scores are experienced as highly subjective and simplistic, a characteristic that is actively taken as a positive means to spark debate and reflections upon each other’s work. While professionals feel a degree of control over the meaning of the measures’ outcomes on the ground, this gets lost in cases of (accused) malpractice and legal complaints. What started as notes and insights into a family’s situation, becomes material or evidence in a court or a hearing. The possibility of losing one's job and professional registration puts pressure on how such scores and results are documented and used by the professional and the organisation. Conclusions: A child protection service in The Netherlands is implementing a collection of instruments, meaningful measures, with a three-tiered aim: to be valuable to professionals, families and the organisation. This study aimed to understand how the meaning of these measures travels and we have found that while meanings need to be negotiated and are subjective in the daily work of professionals, their discussion is deemed valuable. It is crucial for professionals to understand what information needs to be recorded and to feel safe whilst filling out forms, for translational work to be carried out, and for the legal framework to become more in line with the daily practice of professionals, as these measures will be taken out of the context of the organization and daily practice to facilitate accountability and legal checks.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".