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Record W4409088762 · doi:10.1186/s12909-025-07045-6

Geriatric and gerontological physiotherapy in focus: a cross-sectional survey of education, clinical practice, and service availability across world physiotherapy member nations

2025· article· en· W4409088762 on OpenAlexaff
Ogochukwu Kelechi Onyeso, Chiedozie James Alumona, Augustine C Okoh, Michael C Ibekaku, Michael Kalu

Bibliographic record

VenueBMC Medical Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsDalhousie UniversityYork UniversityMcMaster UniversityImpactUniversity of Lethbridge
Fundersnot available
KeywordsMedicineCross-sectional studyService (business)Physical therapyFocus groupMEDLINEClinical PracticeNursingGerontologyMedical educationFamily medicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The ageing global population necessitates specialised geriatric/gerontological physiotherapy services (GPTS) to address age-related conditions. We explored the current state of geriatric/gerontological physiotherapy (GPT) academic programmes and clinical practice among World Physiotherapy member nations (WPMNs) and identified factors, including socioeconomic indicators, that predicted the GPTS globally. METHODS: We conducted an online cross-sectional survey between April 1 and September 19, 2024, inviting official representatives of the 128 WPMNs to answer questions relating to GPT academic programmes and clinical services and practices. We also extracted the Human Development Index (HDI), life expectancy, and Gross National Income (GNI) per capita for each WPMN from the United Nations Development and World Bank databases. Data was analysed using descriptive statistics, a map, bubble charts, and logistic regression models. RESULTS: Sixty-seven countries (67/128, 52.3% response rate) completed the survey. Among them, 34 (50.7%) and 19 (28.4%) reported having geriatric/gerontology modules in their entry-level and graduate-level physiotherapy programmes, respectively. Additionally, 20 (29.9%) and 13 (19.4%) reported having entry-level geriatric/gerontology clinical placement and graduate-level clinical training, respectively. Physiotherapists were members of interdisciplinary geriatric/gerontological teams in Africa (9/11, 81.8%), Asia Western Pacific (10/16, 62.5%), Europe (15/27, 55.6%), North America Caribbean (4/7, 57.1%), and South America (5/6, 83.3%), but they can only lead the teams in few countries: Africa (1/11, 9.1%), Asia Western Pacific (4/16, 25.0%), Europe (5/27, 18.5%), North America Caribbean (2/7, 28.6%), and South America (2/6, 33.3%). GPTS were more common in countries with graduate-level geriatric physiotherapy academic programmes (OR = 33.47, 95% CI: 1.36, 822.39, p = 0.032) GPT Availability in Practice Act (OR = 41.93, 95% CI: 1.66, 1059.78, p = 0.023), and higher HDI (OR = 5.32e + 07, 95% CI: 49.78, 5.67e + 12, p = 0.003). Europe and North America Caribbean regions had lower older-adult-to-physiotherapist ratios and a higher life expectancy, HDI, and GPTS availability than other World Physiotherapy regions. CONCLUSION: Geriatric/gerontological PT education and clinical practice are evolving, especially at the entry-level training across WPMNs; however, extra effort is required to enhance graduate specialization to cater to the emerging ageing population.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.149
GPT teacher head0.623
Teacher spread0.474 · 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.

Study designObservational
DomainMethods
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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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