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Record W7061330047

Physical, Psychosocial, and Practical Burden of Patients Receiving Care for Age-Related Macular Degeneration in Canada: A Mixed-Methods Qualitative Study

2024· article· en· W7061330047 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsMacular degenerationBlindnessThematic analysisDocumentationQualitative researchVisual impairmentPopulationLow visionStakeholder
DOInot available

Abstract

fetched live from OpenAlex

Pradeepa Yoganathan,1– 3 Bernard Hurley,4 Andrew Merkur,5 Chad Andrews,6 Jennifer A Pereira,7 Larissa S Moniz8 1Department of Ophthalmology and Vision Sciences, University of Toronto School of Medicine, Toronto, ON, Canada; 2Department of Ophthalmology, Visual & Anatomical Sciences, Wayne State University School of Medicine, Detroit, MI, USA; 3Windsor Eye Associates, Windsor, ON, Canada; 4University of Ottawa Eye Institute, University of Ottawa, Ottawa, ON, Canada; 5Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, BC, Canada; 6College of Optometrists of Ontario, Toronto, ON, Canada; 7JRL Research & Consulting Inc., Mississauga, ON, Canada; 8Fighting Blindness Canada, Toronto, ON, CanadaCorrespondence: Larissa S Moniz, Fighting Blindness Canada, 12th Floor, 890 Yonge Street, Toronto, Ontario, M4W 3P4, Canada, Tel +1-416-360-4200, Email lmoniz@fightingblindness.caPurpose: Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss. Documentation of the disease’s description and treatment experience of Canadian patients is limited but of interest given the aging population and resultant implications for healthcare systems. A mixed-methods study was conducted to understand the challenges experienced by patients living in Canada with AMD to identify areas of need and for potential reform.Patients and Methods: Canadian residents with wet or dry AMD were eligible for participation in an online survey and one-on-one telephone interview regarding their disease experience. Participants were recruited via a not-for-profit stakeholder organization and an ophthalmology clinic. Surveys were completed from January–June 2020 and interviews were conducted from November–December 2020, with findings reported using descriptive statistics and thematic analysis, respectively.Results: Findings from 303 survey responses and 20 interviews were analyzed. Most participants indicated their vision loss negatively impacts their ability to conduct daily activities (eg, self care, driving) and leads to constant worry, loneliness, and/or isolation. Participants frequently reported requiring caregiver support, often for eye appointment travel or everyday tasks. Regardless of AMD type, participants reported having several appointments each year, and that time spent travelling to/from and waiting at appointments and related costs were considerable. Although participants receiving anti-vascular endothelial growth factor injections valued treatment, the experience added additional burden related to anxiety, fear, pain, and even greater reliance on caregivers. Many participants indicated they felt poorly informed about their disease and treatment options, particularly at diagnosis, which increased their emotional burden.Conclusion: Patients with AMD living in Canada experience a significant and persistent mental, physical, and financial burden as a direct result of their disease. Improvements to provision of disease-related information, support of daily activities and appointment attendance, and the overall treatment experience could substantially enhance outcomes among the growing population of patients with AMD.Keywords: age-related macular degeneration, Canada, burden of illness, disease experience, survey, interview

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.029
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0120.005
Scholarly communication0.0050.003
Open science0.0030.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.146
GPT teacher head0.598
Teacher spread0.452 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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