The art of public health nursing: using confession <i>technè</i> in the sexual health domain
Bibliographic record
Abstract
AIM: This paper explores the sexual health interview from a critical perspective, and to demonstrate how the confession ritual involved in this interview is implicated in the construction of subjectivities (meaning identities) as well as in fostering self-surveillance (self-regulation). BACKGROUND: The concept of public health depends primarily on several surveillance tools that monitor both the incidence and prevalence rates of certain diseases. Within the subgroup of infectious diseases, sexually transmitted infections comprise a group that is closely monitored. As a result, surveillance techniques, including policing sexual practices, are part of the public health worker's mandate. METHOD: Using a Foucauldian perspective, we demonstrate that confession is a political technology in the sexual health domain. FINDINGS: As one group of frontline workers in the field of sexual health, nurses are responsible for data collection through methods such as interviewing clients. Nurses play an integral role in the sexual health experience of clients as well as in the construction of the client's subjectivity. We strongly believe that a Foucauldian perspective could be useful in explaining certain current client behavioural trends (for example, an avoidance by at-risk groups of interactions with nurses in sexual health clinics) being observed in sexual health clinics across the Western hemisphere. CONCLUSION: Clinicians need to be aware of the confessional nature of their questions and provide requested services rather than impose services that they determine to be important and relevant. By appreciating that the sexual health interview is an invasive and embarrassing sexual confession, healthcare providers and policy-makers may be better able to design and implement more user-oriented, population-sensitive sexual health services.
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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.097 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".