New Connectivities: Civil Society, the Third Sector and Dilemmas for Economically and Socially Sustainable Healthcare Delivery
Bibliographic record
Abstract
The U.K. is now taking highly significant – and historic -- steps to open up the NHS to a wider market Among the proposed changes, as laid out in the recent NHS White Paper, ‘Equity and Excellence: Liberating the NHS’ (2011) and the Health and Social Care Bill (2012), is a greater role for civil society organisations and social enterprise, as well as the private sector (DOH, 2007; 2010).\nThe effects of a more open market in healthcare on civil society groups, however, remain unclear and under-theorized. Traditionally held up as mediators between the state and the communities they serve, they are now being encouraged to perform new roles in a post-welfare world, including functioning as healthcare providers themselves, as well as patient advocates, in a competitive landscape where patients (or service users) can make choices under the ‘any qualified provider’ model laid out in the NHS White Paper (Ashton, 2010). Crucially, how will their traditional connectivities with user communities be affected – for better or for worse? What new relationships and networks are they forming to meet new challenges in this fast-changing landscape? And finally, how sustainable are these models of service delivery in an era of austerity and funding cuts?\nThis project examined these issues by directly engaging with civil society organisations (charities and voluntary groups) state and non-state actors in the health and wellbeing sectors. Data were collected through intensive workshops, focus groups and a symposium led by the researchers and including invited experts from Canada, the Netherlands and the U.K.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.080 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.002 | 0.035 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".