Evidence map›Paper›PMID 41662838›Full record

ArticleHealthcare management forum2026

Fixing Work, Not Workers: Leveraging Artificial Intelligence to Combat Burnout in Canadian Healthcare.

Taylor Martin, Robert Maunder, Gillian Strudwick

Abstract read
In one paragraph

Article in Healthcare management forum, 2026. 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
–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

0 citing papers in PubMed.

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

3 authors.

Taylor MartinInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-2189-1039
Robert MaunderInstitute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
Gillian StrudwickInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-1080-7372

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Burnout among healthcare workers in Canada remains a critical challenge with implications for workforce retention, patient safety, and system sustainability. Traditional responses have often emphasized individual coping strategies rather than structural change. This article argues that Artificial Intelligence (AI) might offer new opportunities to address some of the organizational drivers of burnout. We outline three domains where AI may provide value: (1) enhancing the measurement and understanding of burnout, (2) strengthening workforce planning and operational decision-making, and (3) mitigating workplace risks through process redesign and automation. By shifting attention from "fixing workers" to "fixing work," AI might be part of the "solution" to support healthier, more sustainable healthcare environments.

Indexed as

Artificial IntelligenceBurnout, ProfessionalHealth PersonnelCanadaHumansWorkplace

Identifiers

PMID41662838
PMCPMC13272829

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.