Bibliographic record
Abstract
Background: The monies donated by individual donors to Healthy Minds Canada (HMC), a national charitable organization, have been decreasing since 2006. This Major Research Paper provides HMC with communication strategies to increase its funding. The strategies are based on the literature and on best practices. The literature review explores rhetoric as a method to appeal to donors. Other strategies that are examined include appeal letters, websites, and other communications such as providing donors with communication options and thanking donors. In addition, a comparative analysis is conducted between HMC and two other organizations, the Tourette Syndrome Foundation of Canada (TSFC) and Amyotrophic Lateral Scleroses Ontario (ALS Ontario). Conclusions: The analysis of rhetorical appeals determined that communications with donors should apply all three methods of Aristotle’s rhetorical theory of persuasion. These modes include the appeal to emotions (pathos), the appeal to reason (logos), and the appeal of personality or character (ethos). The exploration of appeal letters showed that HMC should thank donors for past or anticipated support, conclude with pleasantries, use negatively framed local mental health statistics, and include positive and moving stories. Moreover, images should be included where possible. Many recommendations are made for HMC’s website but in particular, engaging in social media is emphasized. Furthermore, in all communications to individual donors, HMC should provide these supporters with choices to receive direct mail or electronic mail, and offer opt-out options. Personal accounts can also be set up on HMC’s website that allow donors to select their communication preferences. It is also suggested that there be four mail-outs per year, the newsletter be recommenced, and thank-you letters be sent out within 24 hours of receiving a donation. Lastly, it is recommended that HMC hire another full-time employee to help with its donor communications.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".