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Record W4321459981 · doi:10.1111/ajr.12967

The missing third in rural health research?

2023· editorial· en· W4321459981 on OpenAlexaboutno aff
Timothy Skinner, Lauren A. Booker, Brad Hodge

Bibliographic record

VenueAustralian Journal of Rural Health · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRural healthMedicineEnvironmental healthFamily medicineRural area

Abstract

fetched live from OpenAlex

As we try to make sense of the inequities in morbidity and mortality experienced by rural individuals and communities, it strikes me that there is a fundamental gap in the rural health literature. Given the risk associated with sleep throughout our evolution, and the fact that we spend about a third of our lives asleep, it would seem logical that sleep has a fundamental role. The absolute necessity is no more obvious than the fact that without sleep our bodies cannot function adequately, and if prolonged can result in death. We know a good night sleep, typically seen as 7–9 h of uninterrupted slumber, serves to reset many of our circadian rhythms and homeostatic systems. While the nature of sleep quality involves a number of different mechanisms, there is a general agreement that good quality sleep is characterised by taking a short amount of time to fall asleep, not too many or too long awakenings during the night and a general pattern of progression through light, deep and REM sleep. Quality sleep has a range of beneficial effects, including, heart health, general physical health, emotional control and cognitive ability. Most recently, research suggests that a good night sleep is associated with the activation of the glymphatic system,1 which serves to clear waste proteins out of the brain. Poor sleep quality has been shown to have a strong association with many acute and chronic diseases. Disrupted sleep has a negative effect on our cognitive function, appetite hormones ghrelin and leptin, our immune system, corticosteroids and social-emotional well-being.2-4 Poor sleep has also been implicated to have an impact on telomerase and the repair of the telomeres on our DNA. Given the effect of poor sleep, the increased risk of diseases such as diabetes, heart disease1, 4 Alzheimer's and mental health is unsurprising. In addition to these, it is also important to consider the negative impacts of sleep disorders, such as obstructive sleep apnoea (OSA) and insomnia. OSA is thought to have a prevalence rate of around 17–20% for women and 34–50% for men. Untreated, OSA places substantial stress on the body and increases the risk of high blood pressure, heart disease, stroke and diabetes and has been postulated to be a major contributor to depression and other mental health problems in adulthood. Furthermore, people with OSA are two and a half times more likely to have a motor vehicle accident than those without, due to decreased reaction times, concentration and increased risk of falling asleep at the wheel. Given the relationship between rurality, obesity and OSA, there is likely a greater incidence of OSA in rural communities. Poor sleep and sleep disorders also have major impact on our pocket not just our health. A 2019 report by DeLoitte estimated that sleep disorders had ‘total health system costs of $0.9 billion in 2019-20’ and ‘other financial costs associated with sleep disorders and their attributable conditions were $13.4 billion in 2019-20’.5 While there is regular discussion of the importance of sleep hygiene, we have a good understanding of many other societally driven things that have a negative impact on our sleep, including thermal comfort, CO2 regulation, noise, overcrowding, physical insecurity, food insecurity, financial insecurity, adverse childhood experiences, irregular and circadian disruptive working patterns, mental health problems, stress, worry, and grief.6, 7 Many of these factors that contribute to a lifetime of poor sleep are directly a function of the social and physical contexts that many disadvantaged communities live with. It is no great surprise to find that recent large-scale epidemiology studies are indicating that sleep is a very big part of the mechanism that link socioeconomic disadvantage to poorer health and illness.8-10 Yet, the importance of sleep is practically unexplored in Australian rural health literature including indigenous communities. When we search the literature, we struggle to find much that examines the sleep quality of rural communities or the prevalence and impacts of sleep disorders such as OSA. A systematic search for studies on OSA in rural communities yielded less than a handful of studies. Similar searches for studies of sleep quality in rural communities also failed to find even a handful of studies. One study from Canada noted that the sleep of teenagers in rural areas was worse and shorter than urban adolescents.11 This was attributed to the longer commuting times to get to high school and the need to be up earlier for the school bus in rural communities. There are plenty of other reasons to think that sleep would be poorer in rural communities. A higher proportion of those living in rural communities are also from lower socioeconomic groups and as such are exposed to more of the social determinants of health impacting on sleep quality. Compounding these disadvantages is the common problem of understaffing in so many areas of education, health and social care in rural communities. This, in turn, can lead to rural staff working longer and more irregular hours, extended periods on call, added to the extensive volunteering work that is undertaken to keep emergency services functional. This begs the question as to why we see so little research published on sleep quality and sleep disorders in rural and remote communities. It could be a function of a lack of specialist practitioners in rural and remote areas and the fact that sleep issues might come under the rubric of an already busy respiratory umbrella. It could be the relatively smaller rural populations have not piqued the interest of industry yet, or it could just be a function of the little value that is placed on sleep in our health professional education. While some may like to approach the experience of sleep as an individual problem, therefore negating the need to explore the role of rurality and socioeconomic status, evidence is beginning to suggest otherwise. Being rural creates a unique melting pot for several risk factors for sleep disorders and therefore requires considered attention. Given it makes up a third of our time alive, how many hours of lectures or clinical practice did you get about sleep. With the increasing ability of everyday technology to monitor sleep, oxygen saturation, heartbeat and other parameters, rural residents will have increasing access to information about their sleep quality and duration. Yet, few practitioners have the knowledge and skills to respond to the needs of these communities. As rural practitioners often have a wider scope of practice, there is an even stronger argument for training on sleep health to be a core component of training to work in rural and remote areas. Whatever the reason, it is clearly an area that warrants attention, with some fundamental questions to ask: do people living in rural and remote Australia have poorer quality sleep than urban counterparts; what is the impact of rural working patterns on sleep quality; is there a higher prevalence of OSA and other sleep disorders in rural and remote Australia. We have a faint suspicion we know the answer to these questions, which leads to the even bigger and more challenging question, how do we address these problems when we know that we are likely always going to have a number of disparities between urban and rural communities. How did you sleep last night? Did you wake up feeling fresh and rested? All three authors contributed equally to concept, writing and reviewing. There is no empirical data.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.001

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 head0.206
GPT teacher head0.471
Teacher spread0.265 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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