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Record W4406766505 · doi:10.62754/joe.v3i8.6026

Multidisciplinary Approach to Managing Infectious Diseases: The Roles of Health Assistants, Hospital Administration, Dentists, Nursing, Laboratory, and Pharmacy Technicians

2024· article· en· W4406766505 on OpenAlex
Abdulrahman Yahya Malaihi, Ahmed bin Abdullah Sultan Al-Otaibi, Hassan Abdullah Al-Zaidani, Abdullah F. Alharthi, Mohammed Althobaiti, Wajd Naser Al Gazlan, Basma Salim Suleiman Al-Balawi, Manal Ibrahim Hawsah, Ashwag Hassan Abdurhman Al Grash, Mohammed Abdullah Alshehri, Salem Homoud Alsaleh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Ecohumanism · 2024
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsPharmacyMedicineMultidisciplinary approachNursingAdministration (probate law)Sociology

Abstract

fetched live from OpenAlex

Infectious diseases are among the major challenges facing healthcare systems today and require an inter-multi-disciplinary approach to their prevention and management. It discusses how nursing, health administration, laboratory, and pharmacy teams put infection control into practice. Nurses perform hygiene and educate patients about the same. Health administrators ensure resource allocation and proper application of policies. Laboratory experts perform timely and appropriate diagnostics to facilitate treatment decisions, and pharmacy teams ensure that AMS initiatives are taken forward. It has been a very crucial contribution of all these teams combined in reducing healthcare-acquired infections and offering the best welfare to the patients. The findings call for increased collaboration with continued upgrades relentlessly in combating infectious diseases within healthcare settings.

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.354
Teacher spread0.337 · 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