Developing inclusive employment: Lessons from Telenor Open Mind
Bibliographic record
Abstract
BACKGROUND: Despite significant gains in legal rights for people with disabilities, the employment rate for individuals with disabilities in many countries remains extremely low. Programs to promote the inclusion of people with disabilities in the workforce can have an important impact on individuals' economic and social prospects, as well as societal benefits. OBJECTIVE: This article aims to explore Telenor Open Mind, a job training program at Norway's largest telecommunications company with financial support from Norway's Labor and Welfare Organization (NAV), which acts as a springboard for individuals with disabilities into the workplace. METHODS: A qualitative case study design was utilized to explore the Telenor Open Mind Program. Drawing on field research conducted in Oslo during 2011, this article explores subjective experiences of individuals involved with the program, through interviews and program observations. RESULTS: Telenor Open Mind's two-year program is comprised of a three month training period, in which individuals participate in computer and self-development courses followed by a 21-month paid internship where participants gain hands-on experience. The program has an average 75% rate of employment upon completion and a high rate of participant satisfaction. Participation in the program led to increased self-confidence and social development. The company experienced benefits from greater workplace satisfaction and reductions in sick leave rates. CONCLUSIONS: The Telenor Open Mind program has provided benefits for participants, the company, and society as a whole. Participants gain training, work experience, and increased employability. Telenor gains dedicated and trained employees, in addition to reducing sick leave absences among all employees. Finally, society benefits from the Open Mind program as the individuals who gain employment become tax-payers, and no longer need to receive benefits from the government.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".