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Record W1557216421

Current Trends in the Selection, Training and Support of Australian and Canadian Volunteers: The Issue of Volunteer Stress

2007· article· en· W1557216421 on OpenAlexaboutno aff
Nikola Balvin, Jackie Bornstein, Di Bretherton

Bibliographic record

VenueQueensland's institutional digital repository (The University of Queensland) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)Public relationsTraining (meteorology)Project commissioningMedical educationSoftware deploymentPersonnel selectionPublishingVolunteerSelection (genetic algorithm)PsychologyPolitical scienceMedicineManagementSociologyEngineeringLawSocial science
DOInot available

Abstract

fetched live from OpenAlex

Recent studies of humanitarian aid fieldwork report increased stress levels among workers, urging agencies to improve pre-departure training and field support. The first part of the present study examined agency selection, training and support mechanisms, while the second part examined volunteers’ perceptions of their field placements. Representatives from four Australian and three Canadian volunteer sending agencies participated in structured interviews, revealing that agencies in these countries operate similarly, but most need some improvement in their volunteer selection, training and support processes. Particular attention is needed in areas of stress management and re-entry shock. In the second part, thirteen Australian and five Canadian volunteers from the interviewed agencies participated in focus groups. Participants reported gaining valuable skills, cultural knowledge and career prospects, but also indicated that more stress management training was needed before deployment and upon re-entry into the home culture. The findings lead to recommendations for cooperation between agencies, governments and academic institutions to improve and broaden the applicability of volunteer skills and experiences. Suggestions for future research are also made.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 teacher head, 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".

Quick stats

Citations6
Published2007
Admission routes1
Has abstractyes

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Same venueQueensland's institutional digital repository (The University of Queensland)Same topicTourism, Volunteerism, and DevelopmentFrench-language works237,207