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Record W4395469541 · doi:10.4314/rjmhs.v7i1.1

Assessment of Patient Waiting Time in Primary Health Care Settings in Rwanda: A Mixed-Method Study

2024· article· en· W4395469541 on OpenAlexaff
Immaculate Kyarisiima, Manassé Nzayirambaho, Aimable Nkurunziza, Innocent Twagirayezu

Bibliographic record

VenueRwanda Journal of Medicine and Health Sciences · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsPrimary carePrimary health careMedicineFamily medicinePediatricsNursingEnvironmental health

Abstract

fetched live from OpenAlex

Background: Patient waiting time as an important indicator of quality of services has been a long-standing concern in health care. Objective: The aim of this study was to assess patient waiting time in primary health care settings in Rwanda. Methods: This was a mixed-method study design. In quantitative phase, Patient Flow Time Log was used to track the time patients spent waiting for the service. On exit, a structured questionnaire was administered. Observations were conducted to capture information regarding the flow and processes. In qualitative part, six focus group discussions with patients were conducted. Semi-structured interviews with healthcare providers were held. Results: Among 410 participants, the majority were females (77.1%). The overall health centre level waiting time was 211 minutes (3.5 hours). To receive a service, patients waited an average of 81.5 minutes (1.4 hours). Three conceptual themes were identified: a) reported sections to have long wait time; b) causes of long waiting time; and c) needs for activities to spend time on as patients wait. Conclusion: Most patients experienced prolonged waiting times during their visit to the primary health care settings, and the major factors were the huge number of patients, few healthcare providers, and lack of medical equipment. To effectively address these challenges, more resources and personnel must be allocated to primary healthcare settings to help foster a higher level of client satisfaction with minimal primary healthcare waiting time.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.270
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.492
Teacher spread0.434 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venueRwanda Journal of Medicine and Health SciencesSame topicHealthcare Operations and Scheduling OptimizationFrench-language works237,207