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Record W4226208381 · doi:10.2196/35044

Exploring the Reasons for Low Cataract Surgery Uptake Among Patients Detected in a Community Outreach Program in Cameroon: Focused Ethnographic Mixed Methods Study

2022· article· en· W4226208381 on OpenAlexvenueno aff
Mathew Mbwogge, Henry Nkumbe

Bibliographic record

VenueJMIRx Med · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsCataract surgeryMedicineCataractsEye careOutreachFocus groupOptometryFamily medicineOphthalmology

Abstract

fetched live from OpenAlex

BACKGROUND Cataract, a leading cause of blindness in Sub-Saharan Africa is the clouding effect of the eye’s natural lens that can be treated through surgery. Given the many barriers surrounding cataract surgery, district eye care delivery was emphasized by Vision 2020: The Right to Sight, as one potential way to improve access to eye care. To this effect, the Magrabi ICO Cameroon Eye Institute has been involved in community eye care services since its inception, with more than 1000 sight-restoring cataract surgeries performed among patients referred from outreach camps. That notwithstanding, quite a good number of patients diagnosed with cataracts during community screening camps fail to present for surgery. This study sought to explore some of the challenges to accepting cataract surgery among community-diagnosed cataract patients. OBJECTIVE The objective of this study was five-fold including to (1) assess the level of awareness about cataract and available treatment, (2) explore barriers to cataract surgical uptake, (3) assess people’s perception about the outcome of cataract surgery, (4) understand people’s perception about free cataract surgery, and (5) explore reasons for outright refusal of cataract surgery. METHODS This was an ethnographic study from December 2018 through February 2019 in three different communities of the Lekié Division in Cameroon, in which cataract patients were diagnosed. The study sample was composed of operated cataract patients (identified through the hospital database), cataract blind patients, key informants, and community members identified through a purposeful snowball sampling technique. Focus group discussions, personalized in-depth interviews, and a short demographic questionnaire were used to collect data. Data were analyzed using an excel spreadsheet and Stata 14. Data was presented using tabular and graphical methods. RESULTS A total of 29 subjects (19 males) with a mean age of 54.5±14.5 years took part in the study. The most prominent barriers to cataract surgery were found to be cost (86.2%, 25/29) and fear of surgery (58.6%, 17/29). It was also found that 41.4% of those who do not take up cataract surgery turn to traditional medicine which itself turns out to be a major barrier to cataract surgery. Other barriers included the lack of awareness of available treatment (20.7%), no perceived need (17.24%), cultural beliefs and superstition (13.79%), and negligence (13.79%). CONCLUSIONS This study aimed at exploring the reasons for the low uptake of cataract surgery among community-diagnosed cataract patients. We found that cost (86.2%, 25/29) and fear (58.6%, 17/29) to be the main barriers. Belief in traditional medicine and superstition were the main drivers of fear. The implementation of a tiered pricing system, counselling training for Key Informants, incentives for referral of cataract patients, mass media engagement, advocacy, training and active involvement of traditional doctors as Key Informants, acquisition of a 4X4 outreach van, and motorbikes for camp organizers are some of the recommendations based on our results.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.193
GPT teacher head0.345
Teacher spread0.152 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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