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Record W2606452281

Social workers' interpretations of generalist social work in rural areas

2014· dissertation· en· W2606452281 on OpenAlexaboutno aff
Arja Kilpeläinen, Marjo Romakkaniemi

Bibliographic record

VenueLaCRIS (University of Lapland) · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralist and specialist speciesSocial workWork (physics)SociologyEconomic growthEngineeringEcologyEconomicsBiology
DOInot available

Abstract

fetched live from OpenAlex

The issue of rurality is a concern in many countries, especially with regard to the organisation of welfare services and, although Finland’s geographical and population characteristics qualify it as a rural country, generalist rural social work in rural Finnish municipalities is under-studied. Within this study, I will explicate this phenomenon with the goal of analysing the methods social workers in rural areas use to guide them in identifying their main priorities in conducting social work. The data were drawn from three focus group interviews conducted in rural municipalities in Lapland, which excluded all four cities of the region. Although this study took place in a Finnish context, the phenomenon it addresses exists similarly, at least in some level, in other countries, such as Great Britain, Canada, and Australia. Both the distribution and the comparison of knowledge are needed to promote welfare services in rural areas. Therefore, this study also has international significance. \n \nIn preparing this study, I adapted Ruth Liepins’ method to explore community, in which she holds that community can be analysed through the concepts of meanings, practices, spaces and structures. My focus deviates from her study in that the community is one of the indicators in this study, rather than its target. Because this definition delineates the theoretical approach, the analysis method used in this study was theorybased content analysis. \n \nBased on the focus group interviews, the dimensions of generalist social work can be situated in five, theorybased categories: 1) Geographical and demographic class; 2) Economic class; 3) Structures and spaces; 4) Community; 5) Practices. \n \nAccording to my findings, parts of multidimensional, nuanced generalist rural social work can be universal, but the context and its character relate rural social work specifically to locally-oriented professions. While geographical dimensions do challenge rural social work, they, along with other challenges of population, such as ageing, can also be seen as a resource. Since inhabitants’ livelihood consists of multiple sources, their need for economic support depends on region, its structures and periodic changes in employment opportunities. \n \nMoreover, based on results, the economy of smallness is prevalent in generalist rural social work, where time, local structures and experiential spaces call for it. Local history, too, can hinder generalist social work because the challenges of otherness versus oneness prevail on both professional and personal levels. The organization of generalist rural social work is a crucial factor, and equity in delivering services must also be considered. The importance of nature and green social work is presently under-studied, even when the significance of nature is recognized. Generalist rural social work also makes demands on contemporary research and education. Additional knowledge of generalist rural social work is needed, for both current practice and educational development.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0130.038
Scholarly communication0.0070.004
Open science0.0020.010
Research integrity0.0030.003
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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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