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Record W2979568087

A longitudinal analysis of the chiropractic profession from 1996 to 2007

2018· dissertation· en· W2979568087 on OpenAlexaboutno aff
Mark Fillery

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2018
Typedissertation
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsChiropracticMedicineAlternative medicinePhysical medicine and rehabilitationPsychology
DOInot available

Abstract

fetched live from OpenAlex

Aim: To explore the relative attractiveness of chiropractic in Ontario, Canada from 1996-2007.\nMethods: We conducted a longitudinal cohort study using administrative registration data from 1996-2007. Stickiness and Inflow concepts acted as proxy measures for relative attractiveness. Survival analysis was employed to identify practitioner groups more likely to leave practice.\nResults: Chiropractors grew from 1,955 to 4,185 from 1996-2007 in Ontario. Increases occurred in the proportions of female, and foreign-trained chiropractors. Stickiness indicators averaged changes of 0.29/year from 1997-2003, but from 2004-2007 the average was 8 times greater, at 2.42 points/year. Survival analysis showed that certain groups, like newer practitioners were at greater risk of leaving practice (HRR 1.33, p<0.05; CI 1.04-1.73). However, time-varying analysis showed a post-delisting, increase in profession-wide likelihood of leaving.\nConclusion: The chiropractic profession became less attractive in synchrony with government policy decisions. Following delisting in 2004, the likelihood of leaving practice increased for most chiropractors.

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)0.003
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 score0.968
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.348
Teacher spread0.316 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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