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Record W2120548492

Orthodontic manpower requirements of Trinidad and Tobago.

2012· article· en· W2120548492 on OpenAlexaboutno aff
C O Bourne

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuarter (Canadian coin)CensusPrivate practicePopulationDentistryFamily medicineEnvironmental healthGeography
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: A study was done to estimate the orthodontic manpower requirements of Trinidad and Tobago. METHODS: A questionnaire was administered via e-mail to 9 of 11 orthodontists. Information from a population census, a report on the orthodontic treatment needs of children in Trinidad and Tobago and this questionnaire were used to calculate the number of orthodontists and chairside orthodontic assistants needed in Trinidad and Tobago. RESULTS: On average, 50 per cent of the 289 patients treated by each orthodontist in Trinidad and Tobago annually are children. Approximately, 13 360 patients can be expected to demand orthodontic treatment every year in this country. The number of orthodontists and chairside assistants required to treat these patients was estimated to be 44 and 154, respectively. CONCLUSIONS: Currently, Trinidad and Tobago only has a quarter of the number of orthodontists and orthodontic chairside assistants required to treat the number of patients in need. As the demand is relatively high in Trinidad and Tobago and the number of orthodontists has increased slowly and inadequately for the past decade, the orthodontists are likely to remain adequately employed and happy with their job unlike dentists who are currently in private practice for less than a year.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.226
GPT teacher head0.498
Teacher spread0.272 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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