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Record W3026427159 · doi:10.3389/fpsyg.2020.01024

Self-Reported Patterns of Use of Alcohol and Drugs After Suicide Bereavement and Other Sudden Losses: A Mixed Methods Study of 1,854 Young Bereaved Adults in the UK

2020· article· en· W3026427159 on OpenAlexfundno aff
Alexandra Pitman, Fiona Stevenson, Michael King, David Osborn

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
FundersMedical Research CouncilRoyal Veterinary CollegeUniversity College London Hospitals NHS Foundation TrustQueen's UniversityCardiff Metropolitan UniversityUniversity of WorcesterUniversity of WestminsterQueen's University BelfastCranfield UniversityUniversity of DundeeUniversity of OxfordUniversity College LondonUniversity of SouthamptonKing's College LondonGuarantors of BrainUniversity of ChesterUniversity of GreenwichHeriot-Watt UniversityBishop Grosseteste UniversityDe Montfort UniversityQueen Margaret UniversityUniversity of BedfordshireLiverpool John Moores UniversityStaffordshire UniversityCardiff UniversityBournemouth UniversityUniversity of LeedsLondon Metropolitan UniversityNational Institute for Health and Care ResearchUlster UniversityUniversity of Cumbria
KeywordsSuicide preventionInjury preventionPoison controlLogistic regressionPsychiatryHuman factors and ergonomicsPsychologyAlcohol use disorderSubstance abuseCoping (psychology)Clinical psychologyOccupational safety and healthYoung adultMedicineAlcoholMedical emergencyDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

Background: Bereavement, particularly by suicide, is associated with an excess risk of mortality and of physical and psychological morbidity. Use of alcohol as a coping mechanism is suggested as a contributing factor. However, studies describing substance use after bereavement rely on diagnostic data, lacking a more fine-grained understanding of patterns of substance use when grieving. We aimed to use mixed methods to compare patterns of substance use after bereavement by suicide and other sudden deaths among young adults in the UK. Methods: Using an online survey throughout 37 UK higher education institutions we collected free text responses from 1,854 young adults who had experienced sudden bereavement. We conducted content analysis of free text responses to an open question about patterns of alcohol and drug use following the bereavement, measuring frequencies of coded categories. Collapsing these categories into binary outcomes reflecting increased use of alcohol or drugs, we used multivariable logistic regression to quantify the associations between mode of bereavement and increased post-bereavement substance use. Results: Of 1,854 eligible respondents, 353 reported bereavement by suicide, 395 by accidental death, and 1,106 by sudden natural causes. The majority of the sample reported no increase in their use of alcohol (58%) or unprescribed drugs (85%) after the bereavement. Overall 33% had increased their alcohol use at some point after the bereavement, whilst 12% had increased their use of drugs. People bereaved by suicide were significantly more likely to describe an increase in substance use (adjusted OR=1.29; 95% CI=1.00-1.66; p=0.049) than people bereaved by sudden natural causes, as were people bereaved by non-suicide unnatural deaths (adjusted OR= 1.32; 95% CI=1.03-1.68; p=0.026). Conclusions: Just under half of young UK adults who experience sudden bereavement increase their alcohol use afterwards, and very few increase their use of drugs. People bereaved by suicide or non-suicide unnatural deaths may be more likely than people bereaved by sudden natural causes to use substances as part of the grieving process, and may have a greater need for monitoring of potential harms. Understanding the reasons for substance use will help primary care and bereavement practitioners screen and address needs appropriately.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.049
GPT teacher head0.391
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2020
Admission routes1
Has abstractyes

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