Evidence map›Paper›PMID 36280557›Full record

ReviewCurrents in pharmacy teaching & learning2022

An introduction to text analytics for educators.

Jacqueline E McLaughlin, Kayley Lyons, Carly Lupton-Smith, Kathryn Fuller

Abstract readReview
In one paragraph

Review in Currents in pharmacy teaching & learning, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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.

Jacqueline E McLaughlinDivision of Practice Advancement and Clinical Education, Center for Innovative Pharmacy Education and Research, UNC Eshelman School of Pharmacy, Chapel Hill, NC, United States. Electronic address: jacqui_mclaughlin@unc.edu.
Kayley LyonsCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Victoria, Australia. Electronic address: kayley.lyons@unimelb.edu.au.
Carly Lupton-SmithBloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States. Electronic address: Clutpon1@jhu.edu.
Kathryn FullerDivision of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States. Electronic address: kafuller@email.unc.edu.

Funding

EPIDEMIOLOGY AND BIOSTATISTICS OF AGINGT32AG000247 · NIA · JOHNS HOPKINS UNIVERSITY · PI Karen J. Bandeen-Roche · 1996 to 2026
$13.2M
NIA NIH HHS T32 AG000247
6 · The paper itself

Abstract

our situationEducators often find themselves in possession of large amounts of text-based materials, such as student reflections, narrative feedback, and assignments. While these materials can provide critical insight into topics of interest, they also require a substantial amount of time to read, interpret, and use. The purpose of this article is to describe and provide recommendations for text analytics. METHODOLOGICAL LITERATURE REVIEW: An overview of text analytics is provided, including a brief history, common types of contemporary techniques, and the basic phases of text analytics. Several examples of common text analytics techniques are used to illustrate this approach. OUR RECOMMENDATIONS AND THEIR APPLICATIONS: Practical recommendations are provided to support the use of text analytics in pharmacy education. These recommendations include: (1) clarify the purpose of the text analytics; (2) ensure the research questions are relevant and grounded in the literature; (3) develop a processing strategy and create a dictionary; (4) explore various tools for analysis and visualization; (5) establish tolerance for error; (6) train, calibrate, and validate the analytic strategy; and (7) collaborate and equip yourself. POTENTIAL IMPACT: Text analytics provide a systematic approach to generating information from text-based materials. Several benefits to this approach are apparent, such as improving the efficiency of analyzing text and elucidating new knowledge. Despite recent developments in text analytics techniques, limitations to this approach remain. Efforts to improve usability and accessibility of text analytics remain ongoing, and pharmacy educators should position their work within the context of these limitations.

Indexed as

Data MiningEducation, PharmacyHumansMachine learningSentiment analysisText analyticsText miningTopic modelling

Identifiers

PMID36280557
PMCPMC9904956

What OpenQuestion holds

Textmetadata
LicenceTDM
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.