Decision-making in communication aid recommendations in the UK: cultural and contextual influencers
Bibliographic record
Abstract
High-tech communication aids are one form of augmentative and alternative communication (AAC) intervention offered to children following an assessment process to identify the most appropriate system based on their needs. Professional recommendations are likely to include consideration of child characteristics and communication aid attributes. Recommendations may be influenced by contextual factors related to the cultural work practices and service context of professionals involved, as well as by contextual factors from the child's life including their family environment and wider settings. The aim of this study was to explore the influence of cultural and contextual factors on the real-time decision-making processes of specialized AAC professionals in the UK. A total of six teams were recruited to the study. Each team carried out an assessment appointment related to a communication aid recommendation for a child and family. Following the appointment, each team participated in a focus group examining their decision-making processes during the preceding assessment. Inductive coding was used to analyse the transcribed data, and three organizing themes emerged relating to the global theme of Cultural and Contextual Influencers on communication aid decision-making. An explanatory model was developed to illustrate the funnelling effect that contextual factors may have on decision-making, which can substantially alter the nature and timing of a communication aid recommendation. Implications for clinical practice and future research are discussed.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".