Handling Complaints: Considerations for Prioritizing Complaints
Bibliographic record
Abstract
Overstretched resources and steady increases in the number of complaints filed with the offices of the Quebec Service Quality and Complaints Commissioner prompted us to investigate the complaint-handling systems of health-related organizations operating in Commonwealth and Western European countries. We also examined guidelines used to identify higher priority files (i.e., urgent files). Urgent files can then be prioritized in terms of the time taken to provide a conclusion as well as the depth of the examination. A system where a small fraction of complaints is deemed “urgent” was preferred over systems where complaints are categorized into three or more priority levels, because files categorized in the lowest of three or more priority levels risk being neglected. Applying lessons from other systems and considering the Service Quality and Complaints Commissioner’s mandate, we identified three guiding criteria for determining whether files warrant urgent status: threat to safety, involvement of vulnerable person(s) and risk of recurrence (but only when coupled with safety issues). Since determining which files should be considered urgent is not straight forward, these broad criteria can be adapted and applied on a case-by-case basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".