Bibliographic record
Abstract
Purpose – The purpose of this paper is to outline how nursing has contributed to the development of integrated care in an internationally recognised centre of excellence (Timmins and Ham, 2013). Design/methodology/approach – During a three-week travel scholarship the author undertook interviews, focus groups and observation and has reflected on this through three themes. These are: system working, nursing leadership and examples of integrated care in action. Findings – Elements of the Canterbury approach could have implications for other health care systems, e.g. New Care Models within England. Time was spent on developing the vision, involving many staff. Stability in the senior leadership team allowed decisions to be made in a collective, transformational way. Nurse leadership authenticity meant nursing staff saw integrated decision making being role modelled at a senior level and this appeared to empower them to operate in a similar way. Time was invested in redesign. Creating a positive culture where innovation was tried, without staff feeling the risks and challenges would not be supported by their leaders. Originality/value – This system worked most effectively where there was cohesion between health and social care, and strong relationships developed between leaders and staff working for different providers. The reflection includes practice examples of integrated care services in action. There is potential to inform integrated care developments within other health and social care systems, e.g. Vanguards within England.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".