Original research · 2026-07 edition

AI SEO Statistics: Senior Care (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in senior care.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My 82-year-old dad keeps forgetting to take his heart meds and I live three states away, what are my options for someone to just check in on him daily?
Is it cheaper to hire a caregiver privately through a job board or go through a licensed home care agency?
What is the average hourly rate for non-medical senior home care in a mid-sized city right now?
My mom is being discharged from the hospital after a hip fracture tomorrow and we need someone to help her at home immediately, how do I find emergency care?
How do I tell the difference between home health care and in-home personal care when looking for help for a senior?
What specific questions should I ask a home care agency about their caregiver screening and background check process?
Is it possible to get Medicare to pay for a home health aide to help with bathing and dressing or is that always out-of-pocket?
We are worried about my grandmother's safety at night because she wanders; what kind of overnight home care services actually exist?
Show all 15 questions
What are the biggest red flags to look out for during an initial consultation with a senior care provider?
Can I use a long-term care insurance policy to pay for a companion to come over and cook meals for my parents?
I'm feeling totally burnt out caring for my husband with dementia; what is respite care and how do I hire someone for just a few days a week?
What is the minimum number of hours most home care agencies require per shift or per week?
Should I be looking for a caregiver who is a Certified Nursing Assistant or is a regular companion enough for someone with early-stage Parkinson's?
How do I handle the taxes and liability insurance if I decide to hire a private caregiver directly instead of using a company?
My mom is resistant to having a stranger in the house; what's the best way to introduce a professional caregiver without making her feel like she's losing her independence?

Model by model

18-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about senior care buyers.

Behavior rates across 15 senior care buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%73%47%53%
Suggests DIY first7%13%0%87%
Names specific providers7%20%13%73%
Gives price or cost info7%7%20%67%
Tells to check reviews7%0%0%93%
Tells to verify credentials40%13%7%67%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity13%13%13%80%
Gives selection criteria27%27%20%40%
Warns about red flags7%0%7%87%
Asks a clarifying question40%40%0%40%
Recommends multiple quotes7%7%0%87%

By model

How each assistant handled Senior Care questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same senior care questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 46.7% (Gemini), a 33-point gap on an identical question set.

Across the 15 senior care answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 40%, averaging 624 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 senior care answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 20% of answers (about 0.1 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 13.3%, averaging 332 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 senior care answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 13.3% of answers (about 0.1 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 256 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a senior care buyer to a professional (80%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 624 words on average. Specific providers were named most often by Claude (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a senior care buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Recommends hiring a professional: from 46.7% (Gemini) to 80% (ChatGPT) — a 33-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Suggests a DIY approach first: from 0% (Gemini) to 13.3% (Claude) — a 13-point spread.
  • Names a specific provider: from 6.7% (ChatGPT) to 20% (Claude) — a 13-point spread.

The widest single gap — asks a clarifying question, 40 points — means a senior care buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the senior care market.

Where they agree

The points of near-consensus in Senior Care.

On other behaviors the three models move almost in lockstep — the points of near-consensus for senior care, where all three landed within a few points of each other:

  • Mentions case studies or portfolio: 0% across all three models.
  • Mentions local proximity: 13.3% across all three models.
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
  • Gives selection criteria: 20%–26.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (40%).

Every behavior, measured

All twelve coded behaviors for Senior Care, averaged across the three models.

The behaviors AI models reproduce most often for senior care are recommends hiring a professional (66.7% on average), asks a clarifying question (26.7%) and gives selection criteria (24.5%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (2.2%) and recommends multiple quotes (4.5%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 66.7% on average (ChatGPT 80%, Claude 73.3%, Gemini 46.7%) — a 33-point spread.
  • Asks a clarifying question: 26.7% on average (ChatGPT 40%, Claude 40%, Gemini 0%) — a 40-point spread.
  • Gives selection criteria: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 40%, Claude 13.3%, Gemini 6.7%) — a 33-point spread.
  • Names a specific provider: 13.3% on average (ChatGPT 6.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Mentions local proximity: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Gives price or cost information: 11.1% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 20%) — a 13-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Warns about red flags or scams: 4.5% on average (ChatGPT 6.7%, Claude 0%, Gemini 6.7%) — a 7-point spread.
  • Recommends multiple quotes: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Tells the buyer to check reviews: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the senior care buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the senior care buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 4.5%.

On structuring the decision, a selection-criteria checklist showed up in 24.5% of answers on average and a recommendation to gather multiple quotes in 4.5%. The single least-reproduced protective signal for senior care is "tells the buyer to check reviews" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Senior Care providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 senior care answers, a specific provider was named in 13.3% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for senior care: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Senior Care questions cover.

The 15 questions behind every percentage on this page were drawn from real senior care (home services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact senior care question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific senior care question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →