Socioeconomic and Psychosocial Adversity in Inuit Mothers from Nunavik during the First Postpartum Year / ᐃᓄᓕᕆᓂᕐᒧᑦ ᐱᕙᓪᓕᐊᔪᓕᕆᓂᕐᒧᓪᓗ ᐊᒻᒪᓗ ᐃᓄᓕᕆᓂᒃᑯᑦ ᐃᓱᒪᑎᒍᓪᓗ ᐅᓇᒻᒥᓇᖅᑐᑦ ᓄᓇᕕᒻᒥᑦ ᐃᓄᓐᓄᑦ ᐊᓈᓇᐅᔪᓄᑦ ᐊᕐᕌᒎᑉ ᓯᕗᓪᓕᖅᐹᖓᓂᑦ ᐃᕐᓂᓯᒪᓕᖅᑎᓪᓗᒋᑦ
Bibliographic record
Abstract
The postpartum year is a crucial period for child development and mother-child attachment. In a young and prolific population such as the Inuit from Nunavik (northern Quebec, Canada), postpartum maternal well-being is even more concerning. This study aims to document the prevalence and co-occurrence of socioeconomic and psychosocial risk factors in this population, and to use these factors to identify specific profiles of women. Data collection involved 176 mothers recruited during pregnancy and interviewed 12 months after delivery. Socioeconomic (age, education, single parenting, unemployment, welfare) and psychosocial (psychological distress, suicidal thoughts and attempts, spousal abuse, drug and alcohol use) risk factors were documented. Four high-risk conditions (socioeconomic precariousness, distress, domestic abuse, and substance use) were computed and considered in the analysis. Adversity was salient because most of the women (58%) simultaneously experience many high-risk conditions, with socioeconomic difficulties, distress, and spousal abuse being the most prevalent. Distinct profiles were identified: those without socioeconomic and psychosocial risk factors (30.8%) and those experiencing distress (69.2%). From the latter category, two specific profiles of distressed mothers emerged: single women coping with socioeconomic stressors (40.1%), and women with fewer financial difficulties but in an abusive relationship and more likely to use drugs or binge drink (29.1%). Our results support the need for preventive and public health programs in this population to improve maternal as well as infant wellbeing.ᐊᕐᕌᒍ ᓯᕗᓪᓕᖅᐹᖅ ᐃᕐᓂᓯᒪᓕᖅᑐᓂ ᐱᓪᓗᕆᓐᓂᖅᐸᐅᕗᖅ ᐊᓈᓇᐅᔪᖅ ᕿᑐᕐᖓᖓᓗ ᐊᑕᐅᓯᐅᖃᑎᒌᓐᓂᖏᓐᓄᑦ. ᓄᓇᕕᒻᒥᐅᑦ ᐃᓄᐃᑦ ᐃᓅᓱᑦᑎᓪᓗᒋᑦ ᐊᒻᒪᓗ ᓇᓗᓇᐃᔭᐃᑦᑎᐊᖅᑐᑎᒃ ᑭᒃᑰᓂᖏᓐᓂᒃ ᐃᓱᒫᓗᓇᖅᐳᖅ ᐊᓈᓇᐅᔪᑦ ᖃᓄᐃᓐᖏᓐᓂᖏᑦ ᐃᕐᓂᕋᑖᖅᑐᒥᓂᐅᑎᓪᓗᒋᑦ. ᐅᓇ ᖃᐅᔨᓴᕐᓂᖅ ᑐᕌᒐᖃᖅᑯᖅ ᑎᑎᖅᑐᐃᔾᔪᑕᐅᓪᓗᓂ ᐃᓄᓕᕆᓂᕐᒧᑦ ᐱᕙᓪᓕᐊᓂᕐᒧᓪᓗ ᐊᒻᒪᓗ ᐃᓄᓕᕆᓂᕐᒧᑦ ᐃᓱᒪᒃᑯᓪᓗ ᐊᑦᑕᕐᓇᕈᑕᐅᔪᓂᒃ ᐃᓄᓐᓄᑦ ᐊᒻᒪᓗ ᑖᒃᑯᐊ ᐊᖅᑯᑎᒋᓗᒋᑦ ᐊᕐᓇᐃᑦ ᖃᓄᐃᑦᑑᓂᖏᑦ ᐃᓕᓴᕐᓇᕈᑎᒋᓕᕐᓗᒋᑦ. ᖃᐅᔨᒪᔾᔪᑎᓂᒃ ᑲᑎᖅᓱᐃᓂᖅ ᐃᓚᓕᐅᔾᔨᔪᕗᖅ 176−ᓂᒃ ᐊᓈᓇᐅᔪᓂᒃ ᐃᓚᓕᐅᑦᑐᒋᑦ ᓇᔾᔨᔪᑦ ᐊᒻᒪᓗ ᐊᐱᖅᓱᖅᑕᐅᓯᒪᓪᓗᑎ ᑕᖅᑮᑦ ᖁᓕᑦ ᒪᕐᕉᓪᓗ (12) ᐊᓂᒍᖅᓯᒪᓕᖅᑎᓪᓗᒋᑦ. ᐃᓄᓕᕆᓂᖅ ᐱᕙᓪᓕᐊᓂᒃᑯᑦ (ᐊᕐᕌᒍᒋᔭᖏᑦ, ᐃᓕᓐᓂᐊᕐᓂᖏᑦ, ᐃᓄᑑᔾᔨᓂᖅ, ᐃᖅᑲᓇᐃᔮᖃᕐᓂᖅ, ᓱᒃᑯᐊᕿᖃᑦᑕᕐᓂᖅ) ᐊᒻᒪᓗ ᐃᓱᒪᑎᒍᑦ ᐃᓄᓕᕆᓂᒃᑯᑦ (ᐃᓱᒫᓘᑕᐅᔪᑦ, ᐃᒻᒥᓃᕈᒪᓂᖅ ᐊᒻᒪᓗ ᐃᒻᒥᓃᕋᓱᓐᓂᖅ, ᓂᖓᕐᓂᖅ, ᐋᖓᔮᕐᓇᑐᑦ ᐊᒻᒪᓗ ᐃᒥᐊᓗᒻᒥᒃ ᐊᑐᕐᓗᕐᓂᖅ) ᑕᐃᒪᐃᑦᑐᑦ ᐊᑦᑕᕐᓇᕈᑕᐅᔪᑦ ᑎᑎᖅᑐᖅᑕᐅᓯᒪᕗᑦ. ᑎᓴᒪᑦ ᖁᑦᑎᓂᖅᐹᑦ ᐊᑦᑕᕐᓇᕈᑕᐅᔪᑦ ᐊᑐᖅᑕᐅᔪᑦ (ᐃᓄᓕᕆᓂᕐᒧᑦ ᐱᕙᓪᓕᐊᔪᓕᕆᓂᕐᒧᑦ ᐊᑦᑐᐃᓗᖅᑯᑏᑦ, ᐃᓱᒫᓗᒍᑎᑦ, ᓂᖓᕐᓂᖅ ᐊᒻᒪᓗ ᓇᕐᓚᒍᑎᓂᒃ ᐊᑐᕐᓂᕐᓗᒃ) ᕿᒥᕐᕈᔭᐅᔪᔪᑦ ᐊᒻᒪᓗ ᐃᓱᒻᒥᕆᐊᕈᑕᐅᔪᔪᑦ ᕿᒥᕐᕈᓂᒃᑯᑎᒍᑦ. ᐊᑲᕐᕆᓐᖏᒍᑕᐅᔪᑦ ᓲᔪᕐᓇᑦᑎᐊᔪᕗᑦ ᐅᐱᓐᓇᕋᓂ ᐃᓄᒋᐊᓐᓂᖅᓴᐃᑦ ᐊᕐᓇᐃᑦ (58%) ᖁᑦᑎᓂᖅᐹᖑᔪᓂᑦ ᐊᑦᑕᕐᓇᕈᑎᓂᑦ ᐊᑐᖅᓯᒪᔪᑦ ᐊᑲᐃᓪᓕᐅᕈᑎᖃᖅᑐᑎ ᐃᓄᓕᕆᓂᕐᒧᑦ ᐱᕙᓪᓕᐊᔾᔪᑎᒃᑯᑦ, ᐃᓱᒫᓘᑎᖃᐅᖅᑐᑎ ᐊᒻᒪᓗ ᓂᖓᖅᑕᐅᓂᒃᑯᑦ ᓲᔪᕐᓇᓛᖑᔪᓪᓗᑎ. ᐊᔾᔨᒌᓐᖏᒍᑕᐅᔪᓪᓗ ᓲᔪᕐᓇᖅᓯᔪᕗᑦ: ᑕᐃᒃᑯᐊ ᐃᓄᓕᕆᓂᒃᑯᑦ ᐱᕙᓪᓕᐊᔪᓕᕆᓂᒃᑯᑦ ᐊᒻᒪᓗ ᐃᓱᒪᒃᑯᑦ ᐃᓄᓕᕆᓂᒃᑯᑦ ᐊᑦᑕᕐᓇᕈᑎᖃᓐᖏᑦᑐᑦ (30.8%) ᐊᒻᒪᓗ ᐅᖁᒪᐃᓪᓕᐅᖅᑐᑦ (69.2%). ᑭᖑᓪᓕᐅᔪᒥᒃ ᖃᐅᔨᔾᔪᑎᒥᒃ, ᒪᕐᕈᐃᓕᖅᑲᖓᔫᒃ ᐃᓕᓴᕐᓇᖅᓯᔪᕘᒃ ᐊᓈᓇᐅᔪᓄᑦ ᐅᖁᒪᐃᓪᓕᐅᕈᑕᐅᔪᑦ: ᐃᓄᑑᔾᔨᔪᑦ ᐊᕐᓇᐃᑦ ᐃᓄᓕᕆᓂᒃᑯᑦ ᐱᕙᓪᓕᐊᓂᒃᑯᑦ ᐃᓱᒫᓘᑎᓖᑦ (40.1%) ᐊᒻᒪᓗ ᐊᕐᓇᐃᑦ ᐃᓱᒫᓘᑎᖃᓐᖏᓂᔅᓴᐃᑦ ᑮᓇᐅᔭᑎᒍᑦ ᑭᓯᐊᓂᓕ ᓂᖓᖅᑕᐅᕙᑦᑐᑦ ᐊᒻᒪᓗ ᐋᖓᔮᕐᓇᑐᖅᑐᐸᑦᑐᑦ ᐅᕝᕙᓗᑭᐊᖅ ᐃᒥᕋᓚᑉᐸᑦᑐᑦ (29.1%). ᖃᐅᔨᔾᔪᑎᕗᑦ ᐃᑲᔪᖅᑐᐃᕗᑦ ᑭᓐᖒᒪᔭᐅᔪᓂᒃ ᓄᖅᑲᐅᒥᔾᔪᑎᔅᓴᑦ ᐊᒻᒪᓗ ᐃᓄᓐᓅᓕᖓᔪᓂᒃ ᐃᓗᓯᓕᕆᓂᕐᒧᑦ ᐃᖏᕐᕋᑎᑕᒐᕐᓂᒃ ᐱᕚᓪᓕᐊᓂᒃᑯᑦ ᐊᓈᓇᐅᔪᓄᑦ ᐊᒻᒪᓗ ᓄᑕᕋᖏᑕ ᐃᓅᑦᑎᐊᕐᓂᖏᓐᓄᑦ.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".