Examining the social construction of surveillance: A critical issue for health visitors and public health nurses working with mothers and children
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To critically examine surveillance practices of health visitors (HV) in the UK and public health nurses (PHNs) in Canada. BACKGROUND: The practice and meaning of surveillance shifts and changes depending on the context and intent of relationships between mothers and HVs or PHNs. DESIGN: We present the context and practice of HVs in the UK and PHNs in Canada and provide a comprehensive literature review regarding surveillance of mothers within public health systems. We then present our critique of the meaning and practice of surveillance across different settings. METHODS: Concepts from Foucault and discourse analysis are used to critically examine and discuss the meaning of surveillance. RESULTS: Surveillance is a complex concept that shifts meaning and is socially and institutionally constructed through relations of power. CONCLUSIONS: Healthcare providers need to understand the different meanings and practices associated with surveillance to effectively inform practice. RELEVANCE TO CLINICAL PRACTICE: Healthcare providers should be aware of how their positions of expert and privilege within healthcare systems affect relationships with mothers. A more comprehensive understanding of personal, social and institutional aspects of surveillance will provide opportunities to reflect upon and change practices that are supportive of mothers and their families.
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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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".