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Record W2766489335

Madrid como ciudad amigable con las personas mayores. Escenarios de diagnóstico para políticas públicas

2017· article· es· W2766489335 on OpenAlexaboutno aff
Vicente Rodríguez Rodríguez, Fermina Rojo Pérez, Gloria Fernández‐Mayoralas

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldMedicine
TopicAging, Health, and Disability
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationPersonaHumanitiesPolitical scienceSociologyArtDemography
DOInot available

Abstract

fetched live from OpenAlex

espanolIntroduccion. La poblacion madrilena esta envejeciendo en un espacio urbano desigual y en un contexto de crisis economica, que puede dar lugar a procesos de exclusion social y dificultar una forma de vida activa. El concepto de ciudad amigable, segun el Protocolo de Vancouver (OMS), surge como instrumento de politicas publicas para evitar estos procesos utilizando herramientas de participacion social, como pilar del envejecimiento activo, que favorecen el diagnostico y la intervencion. Objetivos. Ahondar en el diagnostico de la ciudad de Madrid haciendo un analisis de los apartados del Protocolo, que combinan aspectos formales (vivienda y entorno residencial), relacionados con los servicios publicos (transportes, espacios publicos, servicios sanitarios y sociales), y con otros sociales (participacion social y civica, inclusion social, informacion). Metodologia. Se utilizo la encuesta “Ciudades Amigables”, realizada en 2013 por el Ayuntamiento de Madrid a poblacion con 65 o mas anos en 3 cuestionarios y muestras de poblacion independiente. Se calcularon indicadores por bloque tematico y se realizo un analisis descriptivo con representacion cartografica. Resultados. Se destacan tres aspectos: i) un analisis de la exclusion social de las personas mayores en entornos urbanos segun la literatura cientifica, ii) las principales dimensiones que definen el modelo de ciudad amigable aplicable al municipio de Madrid diferenciando los elementos formales y sociales, como instrumento de participacion social, iii) la valoracion del papel de las personas mayores y de otros agentes sociales participantes en el diagnostico y las politicas que podrian ser aplicables. EnglishIntroduction. The population of Madrid is ageing in an unequal urban space and in a context of economic crisis, which can lead to processes of social exclusion and interfere with an active living. The concept of an age‐friendly city, according to the Vancouver Protocol (WHO), emerges as an instrument of public policies to avoid these processes by means of social participation, as a pillar of active ageing, that favour diagnosis and intervention. Aims. To deep into the diagnosis of the city of Madrid by analysing the sections of the Protocol, which combine formal aspects (housing and residential environment), with other related to public services (transport, public spaces, health and social services), and social services (social and civic participation, social inclusion, information). Methodology. The Age‐Friendly Cities survey, conducted in 2013 by the City Council of Madrid to population with 65 or more years in 3 questionnaires and samples of independent population, was used. Indicators by thematic block were calculated and a descriptive analysis was carried out to support cartographic representation. Results. Three aspects stand out: i) an analysis of the social exclusion of older people in urban environments through the scientific literature; ii) the main dimensions that define the friendly city model applicable to the municipality of Madrid differentiating formal and social elements, as an instrument of social participation, iii) the assessment of the older people’s and other social bodies’ role as agents for designing applicable interventions.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.045
GPT teacher head0.364
Teacher spread0.319 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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