Building a foundation for evidence-based practice: experiences in a tertiary hospital
Bibliographic record
Abstract
The aim of this article is to report on the creation and early innovations of an evidence-based nursing committee in a tertiary hospital. During a post-merger transitional period, the Evidence-based Nursing (EBN) Committee conducted a number of projects, which included selecting an evidence-based practice (EBP) model for nursing; developing a process for prioritising and disseminating research findings to healthcare programmes; and evaluating an important EBP application in direct patient care. Hamilton Health Sciences (HHS), a 1157 bed tertiary hospital providing inpatient and outpatient acute and long term care across several health programmes, is a recent merger of 2 hospitals at 4 sites in a large city in southern Ontario, Canada. Approximately 2900 nurses (2550 Registered Nurses and 380 Registered Nursing Assistants) are employed by HHS in either full or part time positions in health programmes that range from infertility services and obstetrical care to a large and progressive cardiac surgery programme. HHS provides regional services to 2.2 million people in Hamilton and Central South Ontario. In 2000, the Nursing Practice Committee (NPC), a nursing body that represents direct care nurses, sets the general direction for nursing, and is accountable to the Chief Nursing Officer, identified 3 priorities for development. One of these priorities was to develop the use of EBP by nurses at the bedside. Therefore, with accountability to the NPC, the EBN Committee was created and given the mandate of developing the use of EBP at the patient care level and recommending processes that would promote EBP among direct care nurses. In 2002, the mandate and responsibilities were expanded (see table 1⇓). View this table: Table 1 Terms of reference for Hamilton Health Sciences (HHS) Evidence-Based Nursing (EBN) Committee Under the co-leadership of a clinical nurse specialist (BK) and a school of nursing faculty member with a track record in health services …
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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 | 0.095 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".