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Record W4404076789 · doi:10.2196/preprints.64270

Digital Contact Tracing Implementation Among Leaders and Health Care Workers in a Pediatric Hospital During the COVID-19 Pandemic: Qualitative Interview Study (Preprint)

2024· preprint· en· W4404076789 on OpenAlexaboutno aff
Brynn O'Dwyer, Mirou Jaana, Charles Hui, Samia Chreim, Jennifer Ellis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintContact tracingCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Qualitative researchHealth careMedicineNursingVirologyPolitical scienceSociologyComputer scienceInfectious disease (medical specialty)Internal medicineWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

<sec> <title>BACKGROUND</title> Health systems had to rapidly implement infection control strategies to sustain their workforces during the COVID-19 pandemic. Various outbreak response tools, such as digital contact tracing (DCT), have been developed to monitor exposures and symptoms of health care workers (HCWs). Limited research evidence exists on the experiences with these technologies and the impacts of DCT innovations from the perspective of stakeholders in health care environments. </sec> <sec> <title>OBJECTIVE</title> This study aims to identify the factors influencing the adoption of DCT, highlight variations in perspectives across 3 key stakeholder groups concerning the impact of DCT, and provide benchmarking evidence for future pandemic preparedness. </sec> <sec> <title>METHODS</title> Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we conducted an exploratory qualitative study to investigate the implementation and impact of DCT at the Children’s Hospital of Eastern Ontario between December 2022 and April 2023. We conducted 21 semistructured interviews with key stakeholders, including health care administrators (6/21, 29%), occupational health and safety specialists (8/21, 38%), and HCWs (7/21, 33%). Stakeholders were asked about the factors influencing engagement with the DCT tool, organizational-level uptake, the implementation process, long-term use and sustainability of DCT, and unintended consequences. Verbatim transcripts were subject to thematic analysis using NVivo (QSR International). </sec> <sec> <title>RESULTS</title> The implementation of DCT was viable and well received. End users indicated that their engagement with the DCT tool was facilitated by its perceived ease of use and the ability to gain awareness of probable COVID-19 exposures; however, risk assessment consequences and access concerns were reported as barriers (&lt;i&gt;reach&lt;/i&gt;). Participants commonly agreed that the DCT technology had a positive influence on the hospital’s capacity to meet the demands of COVID-19 (&lt;i&gt;effectiveness&lt;/i&gt;). Implementors and occupational specialists referred to negative staffing impacts and the loss of nuanced information as unintended consequences (&lt;i&gt;effectiveness&lt;/i&gt;). Safety-focused communication strategies and having a DCT tool that was human-centered were crucial factors driving staff &lt;i&gt;adoption&lt;/i&gt; of the technology. Conversely, &lt;i&gt;adoption&lt;/i&gt; was challenged by the misaligned delivery of the DCT tool with HCWs’ standard practices, alongside the evolving perceived threat of COVID-19. Stakeholders collectively agreed on the viability of DCT and its applicability to infectious disease practices (&lt;i&gt;maintenance&lt;/i&gt;). </sec> <sec> <title>CONCLUSIONS</title> Hospital stakeholders were highly satisfied with DCT technology and it was perceived as feasible, efficient, and having a positive impact on organizational safety. Challenges related to the alignment and delivery of DCT, alongside the evolving perspectives on COVID-19, posed obstacles to continued adoption by HCWs. Our findings contribute to evidence-based practices and present benchmarks that can inform preparedness for future pandemics and infectious disease outbreaks and help other organizations implement similar technologies. </sec>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0000.002
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 head0.102
GPT teacher head0.422
Teacher spread0.320 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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