Instantiating informatics in nursing practice for integrated patient centred holistic models of care: a discussion paper
Bibliographic record
Abstract
AIM: A discussion on how informatics knowledge and competencies can enable nursing to instantiate transition to integrated models of care. BACKGROUND: Costs of traditional models of care are no longer sustainable consequent to the spiralling incidence and costs of chronic illness. The international community looks towards technology-enabled solutions to support a shift towards integrated patient-centred models of care. DESIGN: Discussion paper. DATA SOURCES: A search of the literature was performed dating from 2000-2015 and a purposeful data sample based on relevance to building the discussion was included. DISCUSSION: The holistic perspective of nursing knowledge can support and advance integrated healthcare models. Informatics skills are key for the profession to play a leadership role in design, implementation and operation of next generation health care. However, evidence suggests that nursing engagement with informatics strategic development for healthcare provision is currently variable. IMPLICATIONS FOR NURSING: A statistically significant need exists to progress health care towards integrated models of care. Strategic and tactical plans that are robustly pragmatic with nursing insights and expertise are an essential component to achieve effective healthcare provision. To avoid exclusion in the discourse dominated by management and technology experts, nursing leaders must develop and actively promote the advancement of nursing informatics skills. For knowledge in nursing practice to flourish in contemporary health care, nurse leaders will need to incorporate informatics for optimal translation and interpretation. CONCLUSION: Defined nursing leadership roles informed by informatics are essential to generate concrete solutions sustaining nursing practice in integrated care models.
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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 | 0.039 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".