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Record W2038602402 · doi:10.2310/7750.2008.07071

Canaries in the Mineshaft: The Dermatology Workforce Shortage in Eastern Ontario

2008· article· en· W2038602402 on OpenAlexaffabout
Caroline E. Heughan, Nordau Kanigsberg, Esiahas Amdemichael, Dean Fergusson, Darcy Ammerman

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2008
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineWorkforceEconomic shortageMentorshipWorkloadFamily medicineDermatologyMedical educationEconomic growthManagement

Abstract

fetched live from OpenAlex

BACKGROUND: The number of dermatology residency positions in Canada has not reflected the growing workforce shortage. Until 2005, all dermatology residents at the University of Ottawa were committed to return to their funding area at the completion of their training. This has left Eastern Ontario with a critical shortage of dermatologists. OBJECTIVE: To survey dermatologists practicing in Eastern Ontario to understand the basis of the workforce shortage and outlook for the future. METHODS: Mailed surveys sent in 1999, 2003, and 2006 to all dermatologists in Eastern Ontario requesting demographic information, workload data, and future career plans. RESULTS: There was a 100% response rate in each survey year. Between 1999 and 2006, the total number of practicing dermatologists decreased from 26 to 23, whereas the average age increased from 51.4 to 57.4 years. The waiting time to see new and returning patients increased, from 5.8 to 18.5 weeks and 4.9 to 11.8 weeks, respectively. Ten of the 23 dermatologists practicing in 2006 plan to retire within the next 5 years. CONCLUSIONS: The inadequate supply of dermatologists in Eastern Ontario will increasingly threaten patient care. This emphasizes the need for additional funding for dermatology training positions, continued mentorship, and improved dermatology training for nondermatologists.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.265
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2008
Admission routes2
Has abstractyes

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