Factors Influencing Implementation of Medical Directives by Registered Nurses: The Experience of a Large Ontario Teaching Hospital
Bibliographic record
Abstract
OBJECTIVE: To understand factors that affect the implementation of medical directives by registered nurses in a large teaching hospital. DESIGN: Qualitative nested case study. PARTICIPANTS AND SETTING: A large multi-site teaching hospital that utilizes over 20 different medical directives was chosen as the setting for this case study. Three distinct medical directives within this setting were selected to obtain maximum variation in the number of individuals involved in a particular directive and type of clinical area. Between March and October 2005, 27 individuals concerned with clinical implementation of these medical directives were interviewed using a semi-structured interview schedule. The registrars of two regulatory bodies that oversee policies related to medical directives and a consultant with expertise in medical directives were also interviewed. Eleven documents related to the use of medical directives were identified using purposive document sampling methods and were included in the study. RESULTS: Implementation of medical directives is influenced by a variety of factors, including nurse confidence and willingness to assume responsibility, the amount of new learning needed to carry out the directive and additional paperwork required. Perceived usefulness of the medical directive, physician support of nurses' use of the directives and frequency of encounter with that type of patient were also important factors. The implementation of a medical directive is a complex process; directives are difficult to write well and often affect the scope of practice of other healthcare professionals. The amount of education and monitoring required to implement a directive needs careful consideration to ensure the appropriate resources are available to support implementation. CONCLUSIONS: Greater attention to the factors that facilitate implementation of medical directives is required in order to implement directives in an efficient and effective manner.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".