The Disability Experience: Living with a Birth Defect Resulting from Thalidomide Exposure
Bibliographic record
Abstract
The drug, Thalidomide, is a classic example of how medicine has the potential to cause us harm. The market flooding of this drug in the 1950’s resulted in the birth of 8 to 10 thousand children with birth defects. Today in Canada this tragedy still affects the lives of approximately 125 individuals. How do these individuals live their lives and what has been the overall impact of their impairment? This article explores the lived experience of a woman born with upper limb phocomelia as a result of Thalidomide exposure. A one-hour unstructured face-to-face interview was conducted. Permission was received from the interviewee to make a voice recording of the interview allowing for a more concrete data review. The interview uncovered 6 primary themes indicating that a physical impairment resulting from thalidomide can have a minimal impact on an individual’s overall quality of life, as long as sufficient support and a positive self identity is present. The interviewee’s accounts suggest that living with disability is a unique experience that can lead to positive outcomes. The ultimate conclusion of this paper is that more extensive research is needed to further represent the voices of the disability community.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".