Building Dialogues Between Medical Student & Autistic Patients
Bibliographic record
Abstract
The Patient Immersion Experience (PIE), part of the MD Program’s longitudinal Physicianship course, pairs medical students with individuals with chronic medical conditions to promote an understanding of the lived experience of illness. In October 2017, medical students AW and SC were matched with patient mentor MF, an autistic person[1] and artist. A year later, in the fall of 2018, MF invited his autistic friend AK to participate in collaborating in developing an “interpretive project”, a required capstone component of PIE organized by faculty-lead, PBM. Transcripts of online Google Doc conversations involving SC, AW, MF and AK, that took place over a 3-month period, were used to create a multimedia learning artifact that was exhibited as part of an annual Patient Appreciation Event organized at the end of the year. Rather than simply focusing on transmission of “information”, with SC and AW (as medical students) asking questions and AK and MF responding to it, a commitment was made to an ongoing mutual exchange of ideas. Four main topics were discussed: 1) the value of open communication with others, 2) how the process of informed consent differs for autistic people, 3) hope for a better future for healthcare, and 4) moving forward. These conversations point to the relationship-enhancing possibilities of open, back-and-forth dialogue as an antidote to monological approaches to medicine, providing insights into ways dialogue can enhance both a sense of agency and relational connections, generate new creative thinking, and promote a more holistic, person-centred approach to healthcare.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".