The role of tertiary and further education and work in the resettlement experience of former refugees in Tasmania
Bibliographic record
Abstract
The purpose of this study was to investigate the role of tertiary and further education (TFE) and work in the resettlement experience of former refugees in Tasmania. It specifically aimed at identifying and exploring the impact of the multitude of factors related to TFE and work engagements of former refugees, that impacted the success of the resettlement experience. <br>Studies have shown that former refugees often have high aspirations for higher education in countries such as the US, Canada, the UK and Australia but various factors hinder their ability to access and perform well (Arar, 2021; Ipek, 2021; Molla, 2022; Nell-Müller, Zlatkin-Troitschanskaia, & Happ, 2021; Nickerson et al., 2022). In addition, little is known about how various work opportunities and TFE engagements such as VET, university pathway and other programs available for former refugees shape (or not) their successful resettlement and well-being in Australia (Webb, Faine, Pardy, & Roy, 2017). This limited information on the role of TFE and work in the resettlement experience of former refugees in Australian context provided the impetus for this study. <br>Taking these gaps into consideration, this study was designed to address the following research questions: <br>1) What are the various TFE and work opportunities experienced by former refugees in Tasmania? <br>2) How have former refugees’ TFE and work engagements facilitated (or not) their successful resettlement in Tasmania? <br>3) What are the other factors associated with their TFE and work that influence the resettlement experience of the former refugees? <br>A social capital framework based on social interaction was utilised as a theoretical framework to understand the impact of various micro, meso and macro levels in the successful resettlement of former refugees, especially in relation to the benefits and challenges of the TFE and work engagements in Tasmania. The study employed exploratory sequential mixed method design. A total of 10 refugee support professionals and 17 adult former refugees from various ethnic backgrounds were invited through various Tasmanian based Organisations that support resettlement (OSRs) before finally sitting for face-to-face interview. Additionally, 65 adult former refugees from various ethnic backgrounds filled up the online survey. Both qualitative and quantitative data were generated from refugee support professionals and former refugees using online survey questionnaires and face-to-face interviews. <br>In order to address the research questions, qualitative data were thematically analysed using six step thematic analysis suggested by Braun, Clarke, Hayfield, and Terry (2019). Furthermore, survey data were analysed in IBM SPSS statistics 25 and StataSE 17, using descriptive statistics (Pearson’s chi-square test and Z-tests) and statistical tools that included exploratory factor analysis and multiple linear regression. The following findings were found. <br>First, former refugees engaged in various formal courses (such as VET, university pathway courses and university degree) and informal learning opportunities (such as car driving training, learning activities run by OSRs and everyday communication class) to support language learning needs, social networking and smooth transition to the university and VET in Tasmania. Similarly, most of the former refugees were found to be engaged in low-skilled or semi-skilled work opportunities in Tasmania. As a result, former refugees’ various objectives and context of engaging in different types of TFE and work was identified. <br>Second, the study identified and explored multiple factors in relation to TFE and work engagement of formal refugees that impacted their successful resettlement experience. The main factors were benefits and challenges of TFE and work engagements that impacted the resettlement experience of former refugees by providing access to career pathway information, enrolment in VET and university, better skilled jobs, extended connection to larger Australian networks and growth in confidence necessary to successfully resettle in Tasmania. <br>Third, this study also identified various social and other factors such as demographic characteristics, family and financial factors, refugee targeted programs and policies run by Organisations that support resettlement (OSRs) in Tasmania and former refugees’ social support system that had significant implications over their success of their resettlement. <br>Finally, this study proposed a capitals framework model to explain resettlement success. The model suggested that the quality, amount and varieties of capitals: social capital, human capital, navigational capital, cultural capital and resistant capital, influenced the successful resettlement experience of former refugees in Tasmania.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".