Grassroots inter-professional networks: the case of organizing care for older cancer patients
Bibliographic record
Abstract
Purpose The purpose of this paper of inter-professional networks is to analyze the evolution of relationships between professional groups enacting new forms of collaboration to address clinical imperatives. Design/methodology/approach This paper uses a case study based on semi-structured interviews with physicians and nurses, document analysis and informal discussions. Findings This study documents how two inter-professional networks were developed through professional agency. The findings show that the means by which networks are developed influence the form of collaboration therein. One of the networks developed from day-to-day, immediately relevant, exchange, for patient care. The other one developed from more formal and infrequent research and training exchanges that were seen as less decisive in facilitating patient care. The latter resulted in a loosely knit network based on a small number of ad hoc referrals while the other resulted in a tightly knit network based on frequent referrals and advice seeking. Practical implications Developing inter-professional networks likely require a sustained phase of interpersonal contacts characterized by persuasion, knowledge sharing, skill demonstration and trust building from less powerful professional groups to obtain buy-in from more powerful professional groups. The nature of the collaboration in any resulting network depends largely on the nature of these initial contacts. Originality/value The literature on inter-professional healthcare networks focusses on mandated networks such as NHS managed care networks. There is a lack of research on inter-professional networks that emerged from the bottom up at the initiative of healthcare professionals in response to clinical imperatives. This study looks at some forms of collaboration that these "grass-root" initiatives engender and how they are consolidated.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".