Leading the resettlement of adults with profound learning difficulties from hospital accommodation to supported housing in the community
Bibliographic record
Abstract
Purpose – The purpose of this paper is to provide an analytical description of the leadership provided by an official identified as “P” in the resettlement of adults with profound learning difficulties from hospital care to supported housing in the community. His story, presented as a case study, is contextualised in the history of the resettlement and its policy context, and in the evaluation of the resettlement. Design/methodology/approach – This is a case study of the leadership activities and style of an individual based on evidence from a series of interviews; documentary evidence; and the results of a formal evaluation. Findings – The leadership was highly effective in achieving a resettlement which had to overcome numerous hurdles and which achieved externally evaluated outcomes in improving the quality of life of the service users concerned. Research limitations/implications – This is a case study of an individual with the attendant difficulties of scientific generalisation. The achievements of the individual in terms of outcomes were evaluated through the use of valid and reliable measures. Practical implications – The descriptions of leadership behaviour and style and the obstacle overcome should be illuminating to those facing comparable management challenges. Originality/value – This would be the only case study in the literature of leadership in this area. The evaluation which measures its success is also unique.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".