A retrospective chart review and thematic analysis of patients seeking mpox vaccination during the initial outbreak in 2022–2023: evaluation of access, motivations, and stigma
Bibliographic record
Abstract
BACKGROUND: Mpox was identified in many previously non-endemic countries, including Canada, as of May 2022. In response to the increase in cases in Canada, and more specifically the province of Ontario, the vaccine Imvamune was rolled out. Eligibility was governed by provincial health authorities, and the response varied by region. In addition, because eligibility language was describing certain types of sexual activity, there was potential for harm. The aim of this study was to further explore the experiences of vaccine recipients as it pertained to obtaining the vaccine, access to information and vaccination, self-assessment of risk, perspectives on vaccine rollout, stigma, and community support. METHODS: As a part of care, a clinic in downtown Toronto, Ontario, began hosting mpox immunizations clinics between July of 2022 and March of 2023 with a standard set of clinical intake questions. Following this period, we conducted a retrospective chart review of 113 Imvamune vaccine recipients. Both descriptive quantitative data and thematic qualitative analysis was completed. RESULTS: One hundred thirteen patients received one or two doses of Imvamune between July 2022 and March 2023. The average age was 49 (range 17-78). Patients were not asked sex or gender; however, 111 patients had a male sex listed on their health card and three female sex, one of whom self-identified as a transwoman, with the remainder not having had their sex inputted into their medical records. Through descriptive thematic analysis, this study found the following recurrent themes mentioned by patients in the data set: 1) eligibility, 2) rollout and access, 3) mis/information in the media, 4) stigma. CONCLUSIONS: There is little Canadian data on mpox vaccine rollout beyond epidemiologic and cohort information. Understanding the difficulties and stigma that were faced by vaccine recipients is crucial to ensure that when a public health initiative is initiated, that past traumas are not replicated. This study provides valuable patient perspectives in how to improve ongoing rollout, as well as how a campaign that includes a sexual health component may be more sensitively considered in the future.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".