Calgary Thrives: Data sharing and linkage in the not-for-profit sector.
Bibliographic record
Abstract
IntroductionCompared to the public sector, not-for-profits are less focused on cross-agency data linkage. Technical capacity is often secondary to addressing caseloads and protecting clients. Clients’ privacy is paramount and can be perceived as a barrier to collaboration between agencies. However, collaboration could streamline referrals and better assist vulnerable populations.
 Objectives and ApproachSix not-for-profit agencies in Calgary, Canada participated in a data sharing project to measure various aspects of poverty and link data to determine cross-agency service usage. With this goal in mind, agencies examined their consent and data sharing practices to assess barriers to data sharing. There was a thorough exploration of client consent and how a client’s context can enable or limit data sharing. Cross-agency program usage was assessed among participating agencies using a privacy-preserving record linkage (PPRL) methodology.
 ResultsAmongst the six participating agencies, four were deemed to have adequate technical capacity to share data. A contributing factor to the willingness of agencies to share data was the development of LinkWise: a PPRL software created and developed by PolicyWise. Linkage rates amongst three agencies were compared. Rates ranged from 47.8% to 0.23%. A higher linkage rate between two agencies indicated a small community based agency which provided many referrals to a larger agency, such as a food bank. Lower linkage rates on client intake may indicate an agency with many clients. It may also indicate differing socio-economic brackets for their clients’ catchment area.
 Conclusion/ImplicationsWhile capacity, caseloads, and privacy protection restrict data sharing, not-for-profit agencies would benefit from a data sharing strategy. Linking data represent opportunities for collaboration within a significantly resource constrained sector. Moreover, it could more effectively address issues of vulnerable populations, streamline referrals for services, and facilitate quality improvement.
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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 | 0.006 | 0.002 |
| 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 | 0.003 |
| Open science | 0.004 | 0.001 |
| 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".