Supporting people with traumatic brain injury in their use of public spaces
Bibliographic record
Abstract
Aim. – During the course of the Citizen Accompaniment for Community Integration (APIC) project, people with a traumatic brain injury (TBI) visited several different public places. This study aims to identify and record the facilitating factors and obstacles encountered when engaging in activities in public places.Methodology. – The research design is qualitative. The study is a retrospective analysis of part of the data from the original research, drawn from semi-structured interviews recorded and transcribed verbatim, and from weekly entries in journals kept by the citizen-accompaniers. The sample is made up of 13 individuals with mild, moderate or severe TBI, between the ages of 29 and 69, and nine accompaniers.Results. – Participants’ comments regarding their use of public places, as well as the accompaniers’ thoughts on which factors promote or impede participation in activities in public places were collected according to the sequence of actions framework: the planning, the trip, and the use of the public place.Discussion and conclusion. – The results show that the design of public spaces must take into account the needs for comfort and safety of people with a disability and promote their autonomy and efficiency within the space.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".