Assembling packs: Outreach nurses, disaffiliated persons, and sorcerers
Bibliographic record
Abstract
Nurses working in outreach capacities frequently encounter disaffiliated or 'hard to reach' populations, such as those experiencing homelessness, those who use substances, and those with mental health concerns. Despite best efforts, nurses regularly fail to find meaningful engagement with these populations. Mobilizing the work of Deleuze and Guattari, this paper will critically examine conventional outreach nursing practices as rooted in the royal science of psychiatry, which many 'survivors' of psychiatric interventions reject. The field of Mad Studies offers an understanding of patient resistance to outreach nursing interventions. Delueze and Guattari's concepts of packs and sorcerers provide a framework to envision alternative nursing practices as a form of resistance and creativity, where new alliances may be formed outside the coercive confines of traditional practices. In response to patient resistance, outreach nurses themselves must assemble packs and engage in acts of sorcery.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".