No Decision About Us Without Us: Informing Rare Disease Policies in Canada with the Lived Experiences of Patients and Families with Rare Genetic Lysosomal Storage Diseases
Bibliographic record
Abstract
Canadians born with rare diseases (RDs) experience a myriad of challenges as they navigate the healthcare system to access lifesaving drugs and related services. Canada lags behind other advanced industrialized countries in implementing a comprehensive national strategy to manage RDs and facilitate access to drugs for rare diseases (DRDs). The existing provincially managed system for accessing DRDs is fragmented, uneven, and uncoordinated. It is not reflective of patient experiences but rather aggravates patient challenges, including delayed access to treatment and inconsistent decision-making for drug coverage. The central purpose of this dissertation is to understand how lived experiences of RD patients can inform health policy and the healthcare system to improve RD care. Drawing from semi-structured interviews with RD (lysosomal storage diseases) patients and their families and a review of policy documents, this dissertation uncovers four major challenges encountered by RD patients beyond gaining access to DRDs. These include 1) difficulty in obtaining a timely correct diagnosis; 2) lack of coordinated, efficient, and holistic patient care; 3) lack of consideration of patient voice in decision-making processes; and 4) difficulty in navigating the healthcare system due to stigmatization. This dissertation also finds that the patchwork of programs that govern access to DRDs in Canadian provinces has been ineffective and has failed to support patients in receiving timely and equitable access to DRDs. The above factors demonstrate the necessity for a comprehensive national strategy for RDs that goes beyond an orphan drug framework and addresses the holistic needs of the patient population. Patients and families must be centrally included in the continuum of care and the policymaking process. Such a framework empowers people affected by RDs and reduces their marginalization and exclusion. This dissertation fills important gaps in the existing literature. It delivers important data and insights 1) by collecting extensive, hitherto unavailable, experiential data from RD patients and their families by bringing their unique voices to the policy table; 2) by making patient-centered recommendations for the proposed national RD strategy; and 3) by offering a structured patient engagement framework in the RD sector to meaningfully engage RD patients in decision-making.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| 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".