MétaCan
Menu
Back to cohort
Record W2135316078 · doi:10.1093/bjsw/bcp010

Have Communication Technologies Influenced Rural Social Work Practice?

2009· article· en· W2135316078 on OpenAlexaffabout
Keith Brownlee, John R. Graham, Eric Doucette, Nicole Hotson, Gloria Halverson

Bibliographic record

VenueThe British Journal of Social Work · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSocial workSociologyWork (physics)Public relationsPolitical scienceEconomic growthEngineeringEconomics

Abstract

fetched live from OpenAlex

Recent advances in communication technologies have the capacity for addressing many of the challenges identified with rural and remote social work practice, such as scarcity of professional resources, professional isolation and limited access to supervision and professional development. The purpose of this exploratory, qualitative study was to examine how developments in communication technologies have influenced the way social workers practise social work in rural and remote Canadian areas. In-depth interviews were conducted with thirty-seven clinicians. The findings suggested that having access to communication resources, such as the internet, Telehealth and Telepsychiatry, appears to be positively addressing some issues of rural and northern practice. While the role of communication technologies could be further developed as a means of addressing some of the limitations of distance and fewer professional resources in these areas, it simultaneously risks imposing an urban-centric bias upon social work practice in rural and remote communities.

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)0.003
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 score0.184
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.007
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.025
GPT teacher head0.356
Teacher spread0.332 · 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

Citations63
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe British Journal of Social WorkSame topicSocial Work Education and PracticeFrench-language works237,207