Determinants of the implementation of a new practice in hormonal contraception by Quebec nurses
Bibliographic record
Abstract
In 2007, a task-shifting strategy through which a nurse, in collaboration with a community pharmacist, could start a healthy woman on hormonal contraception without a medical consultation was implemented in the province of Quebec. The purpose of this study was to identify factors associated with the 1) implementation of this new practice by nurses, 2) delay of implementation and 3) intensity of the practice. A validated questionnaire based on Rogers' theory of the diffusion of innovation was sent by postal mail or internet to all nurses that had successfully completed training in hormonal contraception since 2007, were registered at the College of nurses of Quebec and currently worked as nurses. The questionnaire was completed by 745 nurses between November 2011 and March 2012 for a response rate of 26.6%. Results show that implementation of this new nursing practice was more successful when nurses had a high degree of cosmopoliteness, they perceived the new practice as simple, they worked in youth clinics and if health organizations where they worked were open to innovation, had low centralized decision-making and organizational slack. Various attributes of innovation, diffusion networks and characteristics of the organizations also explained intensity of the new practice. The findings suggest new avenues to simplify and scale up this strategy for use in other health organizations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".