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Record W2021834076 · doi:10.1089/acm.2009.0193

The STTEP: A Model for Musculoskeletal Health Care in Marginalized Communities

2009· article· en· W2021834076 on OpenAlexaff
Dein Vindigni, Barbara Polus, Gay Edgecombe, Michael P. Howard, Joan van Rotterdam, Felicity Redpath, Elma Ellen

Bibliographic record

VenueThe Journal of Alternative and Complementary Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsEthosMedicineWork (physics)Conceptual modelHealth careSocial ecological modelHealth promotionPublic relationsPromotion (chess)SustainabilityCommunity healthMusculoskeletal painNursingEconomic growthPhysical therapyPublic healthPolitical scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: This article introduces the STTEP (Sustainable Training, Treatment, Employment Program) Model. The Model has been in operation since 1995. It provides a useful conceptual framework for policy makers, practitioners, and educators. The Model evolved from work carried out by chiropractors, myotherapists, and related health workers in poor communities through the charitable organization Hands On Health Australia. The STTEP Model grew from a recognition that poor communities mostly rely on heavy, repetitive physical labor for work. For these communities, there is little opportunity to access suitable and affordable health care requiring them to frequently live with the pain and disability associated with highly prevalent musculoskeletal conditions in their communities. The STTEP Model includes myotherapy and musculoskeletal health promotion for uncomplicated musculoskeletal conditions. CONCLUSIONS: The Model also supports training for community members and collaborates with community leaders to promote employment opportunities for graduates. The Model embraces an ethos of cultural sensitivity, corporate responsibility, and sustainability. Project Hope (Hands On Philippines Education), a program in the Philippines, is used to illustrate the Model in action.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.038
GPT teacher head0.376
Teacher spread0.338 · 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 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

Citations5
Published2009
Admission routes1
Has abstractyes

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