Nurses' conceptualization and learning of health technology used in practice: An Actor-Network Theory analysis.
Bibliographic record
Abstract
Nurses’ conceptualization and learning of health technology used in practice: An Actor-Network Theory analysis Richard G. Booth, RN, MScN, PhD(c), Mary-Anne Andrusyszyn, RN, EdD, Carroll Iwasiw, RN, EdD, Lorie Donelle, RN, PhD, Deborah Compeau, HBA, PhD The University of Western Ontario, Arthur Labatt Family School of Nursing, London, Canada; The University of Western Ontario, Richard Ivey School of Business, London, Canada Abstract Conceptualizations of health technology in the nursing profession have traditionally been presented in essentialized fashions, favouring either socially-centric, or, techno-centric perspectives. In this qualitative research study, an Actor-Network Theory lensed analysis was used to explore how nurses conceptualize and learn about health technology utilized in practice. Insights and preliminary results stemming from the initial stages of the research study are presented. Background
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".