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Record W2583347761 · doi:10.1108/wwop-01-2017-0001

Ageing well in the right place: partnership working with older people

2017· article· en· W2583347761 on OpenAlexaffabout
Judith Sixsmith, Mei Lan Fang, Ryan Woolrych, Sarah L. Canham, Lupin Battersby, Andrew Sixsmith

Bibliographic record

VenueWorking with Older People · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsService providerGeneral partnershipPublic relationsBusinessContext (archaeology)Affordable housingAutonomyParticipatory action researchService delivery frameworkService (business)NursingSociologyMarketingEconomic growthMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose The provision of home and community supports can enable people to successfully age-in-place by improving physical and mental health, supporting social participation and enhancing independence, autonomy and choice. One challenge concerns the integration of place-based supports available as older people transition into affordable housing. Sustainable solutions need to be developed and implemented with the full involvement of communities, service organizations and older people themselves. Partnership building is an important component of this process. The purpose of this paper is to detail the intricacies of developing partnerships with low-income older people, local service providers and nonprofit housing associations in the context of a Canadian housing development. Design/methodology/approach A community-based participatory approach was used to inform the data collection and partnership building process. The partnership building process progressed through a series of democratized committee meetings based on the principles of appreciative inquiry, four collaboration cafés with nonprofit housing providers and four community mapping workshops with low-income older people. Data collection also involved 25 interviews and 15 photovoice sessions with the housing tenants. The common aims of partnership and data collection were to understand the challenges and opportunities experienced by older people, service providers and nonprofit housing providers; identify the perspectives of service providers and nonprofit housing providers for the provision and delivery of senior-friendly services and resources; and determine actions that can be undertaken to better meet the needs of service providers and nonprofit housing providers in order to help them serve older people better. Findings The partnership prioritized the generation of a shared vision together with shared values, interests and the goal of co-creating meaningful housing solutions for older people transitioning into affordable housing. Input from interviews and photovoice sessions with older people provided material to inform decision making in support of ageing well in the right place. Attention to issues of power dynamics and knowledge generation and feedback mechanisms enable all fields of expertise to be taken into account, including the experiential expertise of older residents. This resulted in functional, physical, psychological and social aspects of ageing in place to inform the new build housing complex. Research limitations/implications The time and effort required to conduct democratized partnerships slowed the decision-making process. Originality/value The findings confirm that the drive toward community partnerships is a necessary process in supporting older people to age well in the right place. This requires sound mechanisms to include the voice of older people themselves alongside other relevant stakeholders. Ageing well in a housing complex requires meaningful placemaking to include the functional, physical, psychological and social aspects of older people’s everyday life in respect to both home and community.

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)0.023
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0220.012
Scholarly communication0.0080.006
Open science0.0020.020
Research integrity0.0020.004
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 head0.028
GPT teacher head0.275
Teacher spread0.248 · 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 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

Citations60
Published2017
Admission routes2
Has abstractyes

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