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Record W2895396160 · doi:10.1080/09638288.2018.1496488

Understanding rehabilitation in Ukraine from the perspective of key informants

2018· article· en· W2895396160 on OpenAlexaff
Anya Archer, Lisa Golec Harper, Debra Cameron

Bibliographic record

VenueDisability and Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsCentre for Disability Prevention and RehabilitationMarkham Stouffville HospitalPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsRehabilitationHealth carePopulationPolitical scienceEquity (law)Public relationsPsychologyMedicinePhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

Background: The evolution of healthcare in Ukraine has been impacted by a number of factors, including years of communist control followed by the birth of an emerging democracy and most recently, conflict in the eastern part of the country. Rehabilitation is an aspect of Ukraine’s healthcare system that is still heavily influenced by the Soviet-era mentality of perfectionism.Methods: This article presents the results of a qualitative research study that undertook 13 key informant interviews to answer the question of what can be learned from the perspectives of individuals in Ukraine or with experience working in Ukraine with respect to developing and implementing appropriate rehabilitation that is inclusive and targets health equity.Results: Key themes that informants determined will affect the future of rehabilitation in Ukraine include the current health care structure, the culture surrounding disability, international and domestic sources of involvement, and a revised curriculum for new and existing rehabilitation professionals.Conclusions: The input from these individuals, supported by evidence from the literature, provides a foundational understanding of the currently fragmented rehabilitation system in Ukraine and the factors that professionals prioritize as integral components of an infrastructure that supports rehabilitation in the twenty-first century.Implications for RehabilitationWhile the recent conflict in Eastern Ukraine has served as a lightning rod to shed light on the lack of resources allocated toward disability and chronic care in the region, rehabilitation is also lacking in the general population, requiring a response that addresses the unique needs of a population of over 44 million individuals.Alongside a curriculum that complies with international accreditation standards, an influx of job and career opportunities developed by the government is needed to encourage individuals to work in the rehabilitation sector.A nation-wide strategy must be developed to disseminate knowledge about disability and rehabilitation in order to begin to address the issues of social exclusion and stigma associated with disability in many post-Soviet countries.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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.

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

Citations4
Published2018
Admission routes1
Has abstractyes

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