Evidence map›Paper›PMID 36381081›Full record

ArticleJournal of medical biochemistry2022

Virtual assessment, training, and evaluation of clinical laboratory technologists amidst peak Covid-19 pandemic: An observational study.

Sibtain Ahmed, Abid Muhammad Abbas, Farhat Jahan, Tomris Ozben

Open access · diamondAbstract read
In one paragraph

Article in Journal of medical biochemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 67% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Sibtain AhmedAga Khan University, Department of Pathology and Laboratory Medicine, Section of Clinical Chemistry, Karachi, Pakistan.
Abid Muhammad AbbasAga Khan University, Department of Pathology and Laboratory Medicine, Section of Clinical Chemistry, Karachi, Pakistan.
Farhat JahanAga Khan University, Department of Pathology and Laboratory Medicine, Section of Clinical Chemistry, Karachi, Pakistan.
Tomris OzbenAkdeniz University, Department of Medical Biochemistry, Antalya, Turkey.
Aga Khan University · PKAkdeniz University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical technologists are considered a neglected group when it comes to academic interventions. We developed and implemented an educational intervention and assessment for the technologists based on an online questionnaire as a pre-test consisting of questions related to knowledge (n=5), attitude (n=3), and practices (n=4) of daily internal quality control (QC) monitoring via Google Docs survey tool. This study served multiple purposes. It allowed keeping the technologists engaged during the peak of the COVID-19 pandemic while also improving the knowledge, attitude, and practices about the internal quality control using Bio-Rad Unity Real Time (URT) QC software. Subjects were graded based on the scores they received out of 100 (0-60 = poor; 61-79 = good; 80-100 = excellent). Training materials, i.e., a set of 5 videos every week via e-mail, were circulated. A voice-over PowerPoint presentation was also shared for easy comprehension. This activity was repeated after one month. A post-test was administered to assess the improvement. The study results show significant improvement in the technologists' performance after the intervention.

Indexed as

clinical chemistryCovid-19laboratoryquality controltechnologistUnity Real Time

Identifiers

PMID36381081
PMCPMC9618329
OpenAlexW4210359909

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.