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Record W4413308441 · doi:10.1177/21501319251364922

Reducing Internally Generated Demand Through Scheduled Communication Time in a Remote Salaried Family Medicine Clinic

2025· article· en· W4413308441 on OpenAlexaffabout
Jake Reaser, Jeffrey DC Irvine

Bibliographic record

VenueJournal of Primary Care & Community Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePrimary careFamily medicineIntensive care medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: In salaried primary care systems, internally generated demand, such as physician-initiated recalls, can limit timely access for other patients. OBJECTIVE: To evaluate whether reserving protected time for unscheduled physician phone calls decreases recall appointment volume. DESIGN: A quality improvement intervention with a before-and-after analysis. SETTING: A salaried family medicine clinic in northern Saskatchewan. PARTICIPANTS: All clinic patients recalled for visits between October 2024 and February 2025 (1005 recalls). INTERVENTION: A daily 30-min block was reserved in each physician's morning schedule for discretionary phone-based follow-up. MAIN MEASURES: Total number of recalls, recall reasons, and patient demographics before and after implementation. KEY RESULTS: Recall appointments dropped by 28.7%, particularly those related to bloodwork, imaging, and specialist communication. However, the time invested in protected communication slots exceeded the time saved from reduced recalls, suggesting a shift in physician workload rather than an overall reduction. CONCLUSION: Allocating protected communication time may support more flexible follow-up and improve task completion efficiency, but does not appear to meaningfully expand physician appointment access.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
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 head0.107
GPT teacher head0.461
Teacher spread0.355 · 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
Published2025
Admission routes2
Has abstractyes

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