How the Movie ‘Out of My Mind’ Brings the F‐Words for Child Development to Life
Bibliographic record
Abstract
This short communication illustrates how the 2024 movie Out of My Mind, starring a protagonist (Melody) with cerebral palsy, embodies the 'F-words' for child development-the paediatric adaptation of the World Health Organization International Classification of Functioning Disability and Health (ICF). The 'F-words' provide a strengths-based and holistic framework to view childhood development, disability and functioning that can benefit service providers, researchers and families alike. If we want the world to adopt the values of the 'F-words', we must bring these values to the world by applying them outside of academia. This short communication outlines how Out of My Mind illustrates and exemplifies each 'F-words' domain, as well as the interconnectedness with other domains, from the perspective of a speech-language pathologist and PhD student in Rehabilitation Science. Key reflections include the impact of social, attitudinal and physical environments on the protagonist's participation and how Fun and Functioning are shown in the movie's first-person perspective. This short communication also highlights the impact of attitudinal, physical and system-level barriers on participation and can inspire us all to change the way we think to shape the society we aspire towards.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".