Using Data for Strategic Partner Outreach in the Newcomer Serving Sector
Bibliographic record
Abstract
Background: This presentation highlights the collaborative journey undertaken by Immigrant Services Calgary (ISC) and its partners leading to the development of the Gateway initiative that focuses on offering seamless support for newcomers while minimizing service duplication. A distinctive partner outreach strategy, informed by real-time data on newcomers’ settlement and integration needs is central to this presentation. Guided by the social determinants of health, ISC’s Partner Success (PS) Team has harnessed aggregated and anonymized client data to identify trends in the newcomer serving sector. This approach has resulted in 80 strategic partnerships spanning across various industries and sectors, achieving 90% partner satisfaction, and maintaining 100% partner retention rate. Our overarching objective is to create a collaborative ecosystem that offers holistic support to newcomers. Methods/Approach: The presentation outlines the utilization of data to guide ISC's approach to: Creating distinct partner segments to enhance the efficacy of the PS Team's efforts. Engaging potential partners based on their alignment with newcomers’ needs. Developing a service directory for planners to make adequate client referrals. Leveraging interconnected digital platforms to facilitate client-service provider connections. Results/Observations: Comprehensive segmentations have strengthened settlement, employment, health, and other support services for newcomers. Gateway planners now utilize the service directory for swift and precise referrals. Partners are utilizing Partner Portal for client referral management. Conclusion: By leveraging data, collaborations can be tailored to meet newcomers' specific needs, fostering better outcomes for them. Additionally, the presentation emphasizes the need for comprehensive consideration of data protection and potential risks while implementing such strategies.
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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 | 0.049 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".