A profile of technology-assisted children and young people in north west England
Bibliographic record
Abstract
AIM: To obtain a profile of children and young people in north west England who needed the ongoing support of medical technology. METHOD: As part of a larger study, 28 community children's nursing teams in the north west of England were asked to profile the children and young people on their caseloads who needed the ongoing support of medical technology. Twenty-five teams returned data, from which a total of 591 children and young people were identified. RESULTS: The most prevalent technology used was gastrostomy/jejunostomy, which was used by more than two-thirds of the sample. Over a quarter of the children/young people were supported by more than one technology. The majority of the children/young people were seven years old or younger Although most had used the technology for five years or less (71 per cent), there were 164 children/ young people who had been technology-assisted for six or more years. CONCLUSION: Although there are limitations in this study, the data is nevertheless useful for planning future services and support, including identifying the numbers of young people who will be transferring to adult services. A more efficient means of collecting these data would be to systematically record long-term conditions and technology assistance in electronic health records.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".