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Record W3000565438

Optimal Life-Careers

2013· book· en· W3000565438 on OpenAlexaboutno aff
Charles P. Chen, Tatijana Busic

Bibliographic record

VenueLAP LAMBERT Academic Publishing eBooks · 2013
Typebook
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoping (psychology)UnderemploymentImmigrationCareer developmentGrounded theoryPsychologySociologySocial psychologyQualitative researchPolitical scienceSocial scienceEconomic growthUnemploymentClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

This book provides an original research study on the life-career development experiences of new and recent immigrant professionals to Canada. Literature addressing Canada’s immigrant professionals has primarily focused on the negative aspects of migration and life-career transition, such as barriers, discrimination and underemployment. Surprisingly few studies have explored how, in spite of personal and environmental barriers, some new Canadians have flourished in their new country. The purpose of this study is to explore the lived experiences of immigrant professionals who believe they have successfully transitioned in the life-career domains. While recognizing difficulties and roadblocks, the book presents a unique insight in the career development field. Twenty individuals were interviewed using a grounded theory approach. Analysis revealed that internal and external factors contributed or hindered their life-career trajectories. Meaning making, social support and behavioural coping emerged as primary coping strategies. Issues with language and accreditation emerged as significant barriers to life-career development. Practical and theoretical implications are discussed.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0030.002
Research integrity0.0100.023
Insufficient payload (model declined to judge)0.0040.007

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 head0.057
GPT teacher head0.374
Teacher spread0.317 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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