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Record W2334679093 · doi:10.1017/s0317167100004170

Inventory of Pediatric Neurology “Manpower” in Canada

2005· article· en· W2334679093 on OpenAlexaffvenueabout
Daniel L. Keene, Peter Humphreys

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsPediatric NeurologyWorkloadDemographicsSpecialtyMedicineNeurologyFamily medicinePediatricsDemographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the demographics and workload characteristics of pediatric neurology in Canada. METHOD: A standardized survey questionnaire was mailed out to practicing pediatric neurologists in Canada in 2001. Variables examined were age, gender, hours on call, regular hours worked per week, type of practice and projected changes in practice over next five to ten years. Results were compared to the 1994 Pediatric Neurology Manpower Survey which had used the same survey instrument. RESULTS: Fifty-six (70%) pediatric neurologists practicing in Canada returned the survey. As was the case in 1994, no significant differences in workload were found based on age or gender. The average age of the practicing pediatric neurologist in 2001 was 51 years compared to 45 years in 1994. The proportion of physicians over 55 years in 2001 was 35% compared to 25% in 1994. CONCLUSIONS: Pediatric neurology in Canada is an aging specialty needing a significant recruitment of new members

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.264
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2005
Admission routes3
Has abstractyes

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