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From knowledge to action: understanding Ghanaian physicians responses to COVID-19 pandemic threats

2024· article· en· W4399248716 on OpenAlexaff
Victor Collins Wutor, Benoit Banga N’guessan

Bibliographic record

VenueInternational Journal of Community Medicine and Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Action (physics)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyPolitical scienceMedicineOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Background: This study, conducted with meticulous care, aimed to determine the knowledge, perception, and preparedness of Ghanaian physicians towards a pandemic or another wave of COVID-19. Methods: The study, conducted between May and July 2023, used a robust methodology and included a comprehensive questionnaire. The questionnaire was distributed through Facebook, WhatsApp, and other social media links, ensuring a broad reach and diverse participation. Results: A total of 777 physicians responded, and participation was from all 16 regions of Ghana. Of these, 372 (47.9%) were males, while 405 were females (52.1%). The survey, consisting of 55 questions about COVID-19 knowledge, 29 questions about perception, and 21 questions about preparedness, was designed to capture a comprehensive understanding. The study’s inclusion criteria were limited to physicians who had direct contact with patients in medical facility settings. The findings revealed that Ghanaian physicians ranked high regarding their knowledge of COVID-19. However, their readiness to face another pandemic challenge was observed at 47% in progress. In comparison, 43% of physicians responded as done, with another 10% not being unaware of the current situation about preparedness. Conclusions: In conclusion, this study sheds light on the nuanced responses of Ghanaian physicians to the COVID-19 pandemic threats, emphasizing the critical role of knowledge in shaping their actions. Through a qualitative exploration, we discerned a spectrum of reactions ranging from proactive measures to adaptive strategies in navigating the uncertainties of the crisis. Ghana needs a blueprint for pandemic management.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.687
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.592
GPT teacher head0.572
Teacher spread0.020 · 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 designNot applicable
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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