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Record W4411344552 · doi:10.46747/cfp.7106417

Administrative burden in primary care

2025· review· en· W4411344552 on OpenAlexaffvenue
Oliver Storseth, Karen McNeil, Agnes Grudniewicz, Rebecca H. Correia, François Gallant, Rachel Thelen, M. Ruth Lavergne

Bibliographic record

VenueCanadian Family Physician · 2025
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsVitalité Health NetworkUniversity of OttawaDalhousie University
Fundersnot available
KeywordsPrimary careData sciencePrimary health careComputer scienceMedicinePrimary (astronomy)World Wide WebFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: Administrative burden contributes to the current primary care crisis. This critical review of the literature explores how primary care administrative burden is discussed, including how it is defined and what drivers and solutions have been identified. DATA SOURCES: A systematic search of MEDLINE and CINAHL electronic databases for peer-reviewed original research articles, literature reviews, and commentaries that discuss administrative burden in the context of primary care or primary health care. STUDY SELECTION: Searches identified 321 articles in MEDLINE and 109 in CINAHL, resulting in a total of 351 articles after duplicates were removed. Based on title and abstract screening, 228 articles were retained for full-text screening; 136 were ultimately included in the analysis. SYNTHESIS: Most articles focused on perspectives of physicians (72.8%), followed by those of other primary care clinicians (14.7%) and patients (12.5%). Few articles explicitly defined administrative burden (n=6), although most illustrated the concept with examples. One relevant definition of administrative burden distinguishes compliance, learning, and psychological costs. This definition was proposed in the context of people interacting with bureaucracies generally, but these categories are also relevant to primary care specifically. Primary care administrative burdens most often included compliance costs (forms and information management), but learning costs (finding information, navigating processes, and adapting to and implementing new technology) and psychological costs (stress and burnout) were also discussed in the literature. Identified drivers of administrative burden included health system requirements, technological tools available to do administrative work, and complexity of patients or patient populations. Technology and task shifting were discussed as both drivers of administrative burden and solutions to administrative workload. CONCLUSION: Examples of administrative burden in primary care underscore that this work often supports central functions of continuity and coordination of care. Attention often focuses on compliance costs, but learning costs (eg, finding information and learning new technology) and psychological costs must not be overlooked. That technology and task shifting can function as both drivers of and solutions to administrative burden highlights why this issue is challenging to address. Solutions should consider costs broadly and evaluate implications from multiple perspectives, including those of patients and caregivers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.100
GPT teacher head0.437
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2025
Admission routes2
Has abstractyes

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