Job Insecurity, Employability and Financial Threat during COVID-19
Bibliographic record
Abstract
COVID-19 has resulted not only in widespread illness and death, it has also upended most spheres of social life including the economic/financial one in that it has had large impacts on local economies, resulting in widespread job loss, job insecurity and loss of income. Employability, a psychological construct, refers to the belief that one can get a (another) job in the event of job loss, and financial threat refers to feelings of threat and anxiety associated with one’s finances. During the pandemic, many people experienced job loss due mainly to business closures. The present study examined the relationship between employability, job insecurity due to COVID-19, and financial threat in a Canadian (n= 487) and U.S. (n=481) sample of adults recruited on MTurk early on in the pandemic (April 2020). Participants in the Canadian sample, compared to their American counterparts, were less likely to be employed full-time, 37% vs. 67%, respectively, were more likely to be unemployed, 40% vs. 13%, respectively, and had lower self-reported socio-economic status. A theoretical model was put forward in which employability was associated with less job insecurity and this was related to less financial threat. Results revealed that financial self-efficacy was associated with greater employability, less job insecurity and less financial threat in both samples. Further, feelings that one had enough income to “get by” since the advent of COVID-19, were positively related to employability in both samples, but in the Canadian sample only, these feelings were also related to less job insecurity and less financial threat. Implications of the study’s results are discussed within the economic climate resulting from the pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".