Directed content analysis: A life course approach to understanding the impacts of the COVID-19 pandemic with implications for public health and social service policy
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had broad impacts on individuals, families and communities which will continue to require multidimensional responses from service providers, program developers, and policy makers. OBJECTIVES: The purpose of this study was to use Life Course theory to understand and imagine public health and policy responses to the multiple and varied impacts of the COVID-19 pandemic on different groups. METHODS: "The Cost of COVID-19" was a research study carried out in Kingston, Frontenac, Lennox and Addington counties in South Eastern Ontario, Canada, between June and December 2020. Data included 210 micronarrative stories collected from community members, and 31 in-depth interviews with health and social service providers. Data were analyzed using directed content analysis to explore the fit between data and the constructs of Life Course theory. RESULTS: Social pathways were significantly disrupted by changes to education and employment, as well as changes to roles which further altered anticipated pathways. Transitions were by and large missed, creating a sense of loss. While some respondents articulated positive turning points, most of the turning points reported were negative, including fundamental changes to relationships, family structure, education, and employment with lifelong implications. Participants' trajectories varied based on principles including when they occurred in their lifespan, the amount of agency they felt or did not feel over circumstances, where they lived (rural versus urban), what else was going on in their lives at the time the pandemic struck, how their lives were connected with others, as well as how the pandemic impacted the lives of those dear to them. An additional principle, that of Culture, was felt to be missing from the Life Course theory as currently outlined. CONCLUSIONS: A Life Course analysis may improve our understanding of the multidimensional long-term impacts of the COVID-19 pandemic and associated public health countermeasures. This analysis could help us to anticipate services that will require development, training, and funding to support the recovery of those who have been particularly affected. Resources needed will include education, mental health and job creation supports, as well as programs that support the development of individual and community agency.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".