Bibliographic record
Abstract
In this chapter, we will use the term ‘social network’ to describe those people and groups with whom an individual has significant social contact. We will first examine the nature and importance of social networks, and will then go on to provide an account of what crisis resolution and home treatment teams (CRTs) can do to maximise the benefits to patients of support from key social relationships, both within the family and across their wider social networks. The nature and importance of social networks While for some the most important social relationships are within the family, for others they include a peer group, friends and acquaintances, neighbours or work colleagues. For most people there is a mix of all of these, varying perhaps with place of residence and state of health, and over time. Human beings are innately social in their behaviour, but it is important to remember that social relationships are not always supportive. For the mental health service user, relationships with and between informal and formal carers, and the relationships within the service between professionals and between the component teams, can have special importance (Chapter 7). Bridgett and Polak (2003a) defined a social network as ‘a series of overlapping social systems: sets of human relationships that vary in size, formality, function and permanence’. At the less-intimate end of the social spectrum, there is an unclear boundary, with a more general social cohesiveness referred to as the social capital of a community.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".