Implementing nursing best practice guidelines: Impact on patient referrals
Bibliographic record
Abstract
BACKGROUND: Although referring patients to community services is important for optimum continuity of care, referrals between hospital and community sectors are often problematic. Nurses are well positioned to inform patients about referral resources. The objective of this study is to describe the impact of implementing six nursing best practice guidelines (BPGs) on nurses' familiarity with patient referral resources and referral practices. METHODS: A prospective before and after design was used. For each BPG topic, referral resources were identified. Information about these resources was presented at education sessions for nurses. Pre- and post-questionnaires were completed by a random sample of 257 nurses at 7 hospitals, 2 home visiting nursing services and 1 public health unit. Average response rates for pre- and post-implementation questionnaires were 71% and 54.2%, respectively. Chart audits were completed for three BPGs (n = 421 pre- and 332 post-implementation). Post-hospital discharge patient interviews were conducted for four BPGs (n = 152 pre- and 124 post-implementation). RESULTS: There were statistically significant increases in nurses' familiarity with resources for all BPGs, and self-reported referrals to specific services for three guidelines. Higher rates of referrals were observed for services that were part of the organization where the nurses worked. There was almost a complete lack of referrals to Internet sources. No significant differences between pre- and post-implementation referrals rates were observed in the chart documentation or in patients' reports of referrals. CONCLUSION: Implementing nursing BPGs, which included recommendations on patient referrals produced mixed results. Nurses' familiarity with referral resources does not necessarily change their referral practices. Nurses can play a vital role in initiating and supporting appropriate patient referrals. BPGs should include specific recommendations on effective referral processes and this information should be tailored to the community setting where implementation is taking place.
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 | 0.022 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".