Professional identities and new technologies of hepatitis C point-of-care testing
Bibliographic record
Abstract
BACKGROUND: New hepatitis C virus (HCV) point-of-care testing technologies need models of care which involve new tasks to be performed by the health workforce (e.g., pathologists, community health workers, and peer workers). This change in tasks may challenge existing core patterns of work and professional identities. This study explores the interactions between professional identity and new technologies of HCV point-of-care testing. METHODS: Between September 2023 and January 2024, in-depth, semi-structured interviews were conducted with people involved in HCV policymaking in Australia. The sample consisted of 29 participants working in seven Australian jurisdictions or nationally: 13 from departments of health, six from community-led organisations, five from local health districts, and five from pathology services. Data were coded according to themes identified in a prior conceptual review of professional identity. Analysis explored the bidirectional relationship between professional identities and the implementation of point-of-care testing. RESULTS: Three themes were identified which explain the role of professional identity in influencing implementation of HCV point-of-care testing. Everyday interpersonal interactions influenced perceptions of risk. Maintaining high quality in point-of-care testing is valued across professions but the interpretation of quality is varied. Workers who deliver services directly to people at risk of HCV emphasise agility as a characteristic of their group identity which also distinguishes them from other professions. CONCLUSION: Professional identities are shaping the rollout of HCV point-of-care testing. The prioritisation of risk, agility, and quality in professional identities shape the possibilities for HCV point-of-care testing. The analysis demonstrates the inextricability of new technology from the people who deliver it.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".