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Record W4405192624 · doi:10.1111/jan.16667

Interpreting Context in Rural and Remote Aged Care Facilities in Readiness for a New Care Model: A Mixed Method Study

2024· article· en· W4405192624 on OpenAlexaboutno aff
Alison Craswell, Karen Watson, Marianne Wallis, Janet Baker, Katharina Merollini, Kaye Coates, Alison Mudge

Bibliographic record

VenueJournal of Advanced Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersHealth Innovation, Investment and Research Office
KeywordsAged careContext (archaeology)NursingMedicineMedical emergencyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Geographical isolation compounds limited access to healthcare services and skilled workforce for the provision of rural aged care. Residents have complex chronic disease management and end-of-life care needs. An undersupply of general medical practitioners due to retirement, attrition or unfilled training places in Australia has impacted recruitment to rural areas. Nurse practitioners have been identified as a potential solution. AIM: To describe and explore the inner (local and organisational) and outer (wider health system) contexts of healthcare, from the perspective of care staff and residents' families. This, in turn, aims to inform the planned implementation of a nurse practitioner model, in several aged care facilities, operating within rural and remote settings, in Queensland Australia. DESIGN: A convergent mixed methods design. METHODS: Qualitative data were collected, in 2022-2023, using semistructured interviews with staff focusing on role, knowledge development, workplace culture and care relationships with local community. Resident's family's perspectives were obtained as a secondary analysis of organisational feedback data. Quantitative data were collected from direct care workers using the Alberta Context Tool for Long-Term Care. Data were analysed according to type and integrated. RESULTS: Relational care for residents and families is highly valued but provision of quality is challenging where time-poor staff are perceived to be doing the best they can. Scarce local healthcare services make it difficult to meet resident healthcare needs. Despite the supportive organisational culture, evolving policy requirements have impacted already difficult staff recruitment in rural settings. CONCLUSION: Identifying contextual needs of organisations in readiness for change highlights geographical and sectoral nuances influencing any future implementation. As government policy changes to improve the older adult care sector, rural and remote facilities are forced to increasingly adapt. IMPLICATIONS FOR THE PROFESSION: Context-specific needs extend far beyond a nurse practitioner providing additional expertise in care provision. IMPACT STATEMENTS: What problem did the study address? Nurse practitioners have been successfully implemented into residential aged care facilities in metropolitan and major regional centres but translating this role into rural and remote Australia requires being cognisant of the needs, unique challenges and context of this setting. What were the main findings? In an organisational culture of support, the importance of staff providing relational care and having connection with older adult residents and families was a central driver. It was challenging for staff to meet complex care requirements in the absence of local healthcare options and support. Time pressures, from inadequate staffing and changing structural aged care sector, force the prioritising of care requirements. Where and on whom will the research have an impact? Older adults, policy makers and aged care providers will benefit from understanding the context of rural and remote settings, particularly in identifying potential solutions when there are gaps in primary and secondary healthcare. REPORTING METHOD: The GRAMMS checklist was followed in reporting of this study. PATIENT OR PUBLIC CONTRIBUTION: Two lived experience consumers were involved as research team members. One was involved during the development and submission of the funding application and another during project activities including data collection and analysis and the development of publications.

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)0.021
metaresearch head score (Gemma)0.015
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0050.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.448
Teacher spread0.410 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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